Overview

Dataset statistics

Number of variables74
Number of observations5142
Missing cells146238
Missing cells (%)38.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.2 MiB
Average record size in memory5.4 KiB

Variable types

Numeric9
Categorical12
Text48
Unsupported2
DateTime1
Boolean2

Alerts

DOI Link has constant value "0.0"Constant
Highly Cited Status has constant value "True"Constant
Hot Paper Status has constant value "False"Constant
Date of Export has constant value "2025-12-30"Constant
Web of Science Record has constant value "0"Constant
180 Day Usage Count is highly overall correlated with Book DOI and 6 other fieldsHigh correlation
Book DOI is highly overall correlated with 180 Day Usage Count and 10 other fieldsHigh correlation
Book Group Authors is highly overall correlated with 180 Day Usage Count and 7 other fieldsHigh correlation
Cited Reference Count is highly overall correlated with 180 Day Usage Count and 2 other fieldsHigh correlation
Document Type is highly overall correlated with Book Group Authors and 1 other fieldsHigh correlation
Language is highly overall correlated with Book DOI and 1 other fieldsHigh correlation
Number of Pages is highly overall correlated with 180 Day Usage Count and 4 other fieldsHigh correlation
Open Access Designations is highly overall correlated with Book DOI and 2 other fieldsHigh correlation
Publication Type is highly overall correlated with Book DOI and 5 other fieldsHigh correlation
Publication Year is highly overall correlated with 180 Day Usage Count and 2 other fieldsHigh correlation
Pubmed Id is highly overall correlated with Language and 5 other fieldsHigh correlation
Since 2013 Usage Count is highly overall correlated with 180 Day Usage Count and 5 other fieldsHigh correlation
Special Issue is highly overall correlated with Open Access Designations and 5 other fieldsHigh correlation
Supplement is highly overall correlated with 180 Day Usage Count and 6 other fieldsHigh correlation
Times Cited, All Databases is highly overall correlated with Book DOI and 6 other fieldsHigh correlation
Times Cited, WoS Core is highly overall correlated with Book DOI and 6 other fieldsHigh correlation
Web of Science Index is highly overall correlated with Book DOI and 2 other fieldsHigh correlation
Language is highly imbalanced (92.3%)Imbalance
Document Type is highly imbalanced (60.8%)Imbalance
Supplement is highly imbalanced (52.4%)Imbalance
Special Issue is highly imbalanced (92.1%)Imbalance
Book Authors has 5100 (99.2%) missing valuesMissing
Book Editors has 3818 (74.3%) missing valuesMissing
Book Group Authors has 4017 (78.1%) missing valuesMissing
Book Author Full Names has 5100 (99.2%) missing valuesMissing
Group Authors has 5140 (> 99.9%) missing valuesMissing
Book Series Title has 3911 (76.1%) missing valuesMissing
Book Series Subtitle has 5142 (100.0%) missing valuesMissing
Conference Title has 2751 (53.5%) missing valuesMissing
Conference Date has 2751 (53.5%) missing valuesMissing
Conference Location has 2751 (53.5%) missing valuesMissing
Conference Sponsor has 3453 (67.2%) missing valuesMissing
Conference Host has 4619 (89.8%) missing valuesMissing
Author Keywords has 457 (8.9%) missing valuesMissing
Keywords Plus has 2366 (46.0%) missing valuesMissing
Abstract has 164 (3.2%) missing valuesMissing
Affiliations has 290 (5.6%) missing valuesMissing
Reprint Addresses has 92 (1.8%) missing valuesMissing
Email Addresses has 454 (8.8%) missing valuesMissing
Researcher Ids has 1794 (34.9%) missing valuesMissing
ORCIDs has 1775 (34.5%) missing valuesMissing
Funding Orgs has 2820 (54.8%) missing valuesMissing
Funding Name Preferred has 2841 (55.3%) missing valuesMissing
Funding Text has 2826 (55.0%) missing valuesMissing
Cited References has 5142 (100.0%) missing valuesMissing
ISSN has 1667 (32.4%) missing valuesMissing
eISSN has 2585 (50.3%) missing valuesMissing
ISBN has 2737 (53.2%) missing valuesMissing
Journal Abbreviation has 1242 (24.2%) missing valuesMissing
Journal ISO Abbreviation has 2443 (47.5%) missing valuesMissing
Publication Date has 2766 (53.8%) missing valuesMissing
Volume has 2192 (42.6%) missing valuesMissing
Issue has 3041 (59.1%) missing valuesMissing
Part Number has 5133 (99.8%) missing valuesMissing
Supplement has 5125 (99.7%) missing valuesMissing
Special Issue has 4937 (96.0%) missing valuesMissing
Meeting Abstract has 5138 (99.9%) missing valuesMissing
Start Page has 1440 (28.0%) missing valuesMissing
End Page has 1440 (28.0%) missing valuesMissing
Article Number has 4175 (81.2%) missing valuesMissing
DOI has 950 (18.5%) missing valuesMissing
DOI Link has 950 (18.5%) missing valuesMissing
Book DOI has 5069 (98.6%) missing valuesMissing
Early Access Date has 3926 (76.4%) missing valuesMissing
Pubmed Id has 4979 (96.8%) missing valuesMissing
Open Access Designations has 3457 (67.2%) missing valuesMissing
Highly Cited Status has 5128 (99.7%) missing valuesMissing
Hot Paper Status has 5128 (99.7%) missing valuesMissing
doi_norm has 950 (18.5%) missing valuesMissing
Times Cited, WoS Core is highly skewed (γ1 = 39.49011734)Skewed
Times Cited, All Databases is highly skewed (γ1 = 40.13761972)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
UT (Unique WOS ID) has unique valuesUnique
Book Series Subtitle is an unsupported type, check if it needs cleaning or further analysisUnsupported
Cited References is an unsupported type, check if it needs cleaning or further analysisUnsupported
Cited Reference Count has 114 (2.2%) zerosZeros
Times Cited, WoS Core has 1472 (28.6%) zerosZeros
Times Cited, All Databases has 1307 (25.4%) zerosZeros
180 Day Usage Count has 2249 (43.7%) zerosZeros
Since 2013 Usage Count has 430 (8.4%) zerosZeros

Reproduction

Analysis started2026-01-14 11:00:37.242254
Analysis finished2026-01-14 11:01:18.731627
Duration41.49 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct5142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2570.5
Minimum0
Maximum5141
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:18.850644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile257.05
Q11285.25
median2570.5
Q33855.75
95-th percentile4883.95
Maximum5141
Range5141
Interquartile range (IQR)2570.5

Descriptive statistics

Standard deviation1484.5119
Coefficient of variation (CV)0.57751872
Kurtosis-1.2
Mean2570.5
Median Absolute Deviation (MAD)1285.5
Skewness0
Sum13217511
Variance2203775.5
MonotonicityStrictly increasing
2026-01-14T11:01:19.022173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51411
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51251
 
< 0.1%
51241
 
< 0.1%
51231
 
< 0.1%
51221
 
< 0.1%
Other values (5132)5132
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
51411
< 0.1%
51401
< 0.1%
51391
< 0.1%
51381
< 0.1%
51371
< 0.1%
51361
< 0.1%
51351
< 0.1%
51341
< 0.1%
51331
< 0.1%
51321
< 0.1%

Publication Type
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size251.2 KiB
J
2665 
C
2349 
B
 
104
S
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5142
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
J2665
51.8%
C2349
45.7%
B104
 
2.0%
S24
 
0.5%

Length

2026-01-14T11:01:19.182512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:01:19.269316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
j2665
51.8%
c2349
45.7%
b104
 
2.0%
s24
 
0.5%

Most occurring characters

ValueCountFrequency (%)
J2665
51.8%
C2349
45.7%
B104
 
2.0%
S24
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J2665
51.8%
C2349
45.7%
B104
 
2.0%
S24
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J2665
51.8%
C2349
45.7%
B104
 
2.0%
S24
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J2665
51.8%
C2349
45.7%
B104
 
2.0%
S24
 
0.5%

Authors
Text

Distinct4729
Distinct (%)92.0%
Missing3
Missing (%)0.1%
Memory size490.9 KiB
2026-01-14T11:01:19.618159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length396
Median length141
Mean length38.615295
Min length5

Characters and Unicode

Total characters198444
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4442 ?
Unique (%)86.4%

Sample

1st rowWeiguoZou
2nd rowRepenning, A; Basawapatna, AR; Escherle, NA
3rd rowShih, WC
4th rowWu, SY
5th rowLu, CJ; Zhang, S; Chen, XQ
ValueCountFrequency (%)
m1203
 
3.4%
a1117
 
3.2%
j892
 
2.5%
s842
 
2.4%
c722
 
2.1%
d596
 
1.7%
r529
 
1.5%
e513
 
1.5%
l475
 
1.4%
k475
 
1.4%
Other values (8296)27755
79.0%
2026-01-14T11:01:20.131145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29980
 
15.1%
,17403
 
8.8%
a12744
 
6.4%
;12296
 
6.2%
e10033
 
5.1%
n8709
 
4.4%
i7779
 
3.9%
o7596
 
3.8%
r7412
 
3.7%
l5102
 
2.6%
Other values (71)79390
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)198444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29980
 
15.1%
,17403
 
8.8%
a12744
 
6.4%
;12296
 
6.2%
e10033
 
5.1%
n8709
 
4.4%
i7779
 
3.9%
o7596
 
3.8%
r7412
 
3.7%
l5102
 
2.6%
Other values (71)79390
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)198444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29980
 
15.1%
,17403
 
8.8%
a12744
 
6.4%
;12296
 
6.2%
e10033
 
5.1%
n8709
 
4.4%
i7779
 
3.9%
o7596
 
3.8%
r7412
 
3.7%
l5102
 
2.6%
Other values (71)79390
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)198444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29980
 
15.1%
,17403
 
8.8%
a12744
 
6.4%
;12296
 
6.2%
e10033
 
5.1%
n8709
 
4.4%
i7779
 
3.9%
o7596
 
3.8%
r7412
 
3.7%
l5102
 
2.6%
Other values (71)79390
40.0%

Book Authors
Text

Missing 

Distinct21
Distinct (%)50.0%
Missing5100
Missing (%)99.2%
Memory size162.1 KiB
2026-01-14T11:01:20.391622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length22
Mean length14.261905
Min length6

Characters and Unicode

Total characters599
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)33.3%

Sample

1st rowAnonymous
2nd rowFonseca, D
3rd rowFelicia, P
4th rowFonseca, D
5th rowRos, M
ValueCountFrequency (%)
d12
 
10.4%
fonseca11
 
9.6%
m6
 
5.2%
ros4
 
3.5%
wang4
 
3.5%
ps4
 
3.5%
neumann3
 
2.6%
md3
 
2.6%
dion3
 
2.6%
l3
 
2.6%
Other values (49)62
53.9%
2026-01-14T11:01:20.768361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Book Editors
Text

Missing 

Distinct515
Distinct (%)38.9%
Missing3818
Missing (%)74.3%
Memory size236.3 KiB
2026-01-14T11:01:21.103271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length455
Median length118
Mean length41.414653
Min length3

Characters and Unicode

Total characters54833
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)23.6%

Sample

1st rowKim, Y
2nd rowRich, PJ; Hodges, CB
3rd rowChang, T
4th rowACM
5th rowBlackwell, A; Plimmer, B; Stapleton, G
ValueCountFrequency (%)
m388
 
4.0%
j335
 
3.5%
a325
 
3.4%
s232
 
2.4%
r165
 
1.7%
g153
 
1.6%
c145
 
1.5%
t141
 
1.5%
d133
 
1.4%
lg121
 
1.3%
Other values (1695)7510
77.8%
2026-01-14T11:01:21.596577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8324
 
15.2%
,4805
 
8.8%
;3517
 
6.4%
a3267
 
6.0%
e2789
 
5.1%
n2316
 
4.2%
r2312
 
4.2%
i2302
 
4.2%
o2281
 
4.2%
l1252
 
2.3%
Other values (46)21668
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)54833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8324
 
15.2%
,4805
 
8.8%
;3517
 
6.4%
a3267
 
6.0%
e2789
 
5.1%
n2316
 
4.2%
r2312
 
4.2%
i2302
 
4.2%
o2281
 
4.2%
l1252
 
2.3%
Other values (46)21668
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8324
 
15.2%
,4805
 
8.8%
;3517
 
6.4%
a3267
 
6.0%
e2789
 
5.1%
n2316
 
4.2%
r2312
 
4.2%
i2302
 
4.2%
o2281
 
4.2%
l1252
 
2.3%
Other values (46)21668
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8324
 
15.2%
,4805
 
8.8%
;3517
 
6.4%
a3267
 
6.0%
e2789
 
5.1%
n2316
 
4.2%
r2312
 
4.2%
i2302
 
4.2%
o2281
 
4.2%
l1252
 
2.3%
Other values (46)21668
39.5%

Book Group Authors
Categorical

High correlation  Missing 

Distinct25
Distinct (%)2.2%
Missing4017
Missing (%)78.1%
Memory size282.3 KiB
IEEE
436 
ACM
386 
Assoc Comp Machinery
100 
ASSOC COMP MACHINERY
76 
ASSOC COMPUTING MACHINERY
 
41
Other values (20)
86 

Length

Max length41
Median length35
Mean length7.9093333
Min length3

Characters and Unicode

Total characters8898
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st rowAssoc Comp Machinery
2nd rowIEEE
3rd rowACM
4th rowIEEE
5th rowIEEE

Common Values

ValueCountFrequency (%)
IEEE436
 
8.5%
ACM386
 
7.5%
Assoc Comp Machinery100
 
1.9%
ASSOC COMP MACHINERY76
 
1.5%
ASSOC COMPUTING MACHINERY41
 
0.8%
Assoc Computing Machinery24
 
0.5%
ASEE14
 
0.3%
Destech Publicat Inc10
 
0.2%
IEEE Comp Soc6
 
0.1%
IEEE COMPUTER SOC5
 
0.1%
Other values (15)27
 
0.5%
(Missing)4017
78.1%

Length

2026-01-14T11:01:21.735176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ieee448
26.7%
acm386
23.0%
assoc246
14.6%
machinery242
14.4%
comp182
10.8%
computing66
 
3.9%
destech15
 
0.9%
inc15
 
0.9%
publicat15
 
0.9%
asee14
 
0.8%
Other values (21)52
 
3.1%

Most occurring characters

ValueCountFrequency (%)
E1510
17.0%
C888
 
10.0%
A784
 
8.8%
M760
 
8.5%
I638
 
7.2%
556
 
6.2%
c319
 
3.6%
S273
 
3.1%
s272
 
3.1%
o271
 
3.0%
Other values (31)2627
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)8898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1510
17.0%
C888
 
10.0%
A784
 
8.8%
M760
 
8.5%
I638
 
7.2%
556
 
6.2%
c319
 
3.6%
S273
 
3.1%
s272
 
3.1%
o271
 
3.0%
Other values (31)2627
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1510
17.0%
C888
 
10.0%
A784
 
8.8%
M760
 
8.5%
I638
 
7.2%
556
 
6.2%
c319
 
3.6%
S273
 
3.1%
s272
 
3.1%
o271
 
3.0%
Other values (31)2627
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1510
17.0%
C888
 
10.0%
A784
 
8.8%
M760
 
8.5%
I638
 
7.2%
556
 
6.2%
c319
 
3.6%
S273
 
3.1%
s272
 
3.1%
o271
 
3.0%
Other values (31)2627
29.5%
Distinct4759
Distinct (%)92.6%
Missing3
Missing (%)0.1%
Memory size541.2 KiB
2026-01-14T11:01:22.170819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length568
Median length186
Mean length58.786729
Min length6

Characters and Unicode

Total characters302105
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4492 ?
Unique (%)87.4%

Sample

1st rowWeiguoZou
2nd rowRepenning, Alexander; Basawapatna, Ashok R.; Escherle, Nora A.
3rd rowShih, Wen-Chung
4th rowWu, Sheng-Yi
5th rowLu Changjin; Zhang Shuai; Chen Xiuqiong
ValueCountFrequency (%)
m283
 
0.7%
a281
 
0.7%
j217
 
0.5%
wang194
 
0.5%
chen177
 
0.4%
li175
 
0.4%
c170
 
0.4%
maria170
 
0.4%
s161
 
0.4%
zhang157
 
0.4%
Other values (12983)37970
95.0%
2026-01-14T11:01:23.064453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34816
 
11.5%
a28585
 
9.5%
e19783
 
6.5%
i19519
 
6.5%
n19497
 
6.5%
,17381
 
5.8%
r14354
 
4.8%
o14072
 
4.7%
;12296
 
4.1%
l9893
 
3.3%
Other values (52)111909
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)302105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34816
 
11.5%
a28585
 
9.5%
e19783
 
6.5%
i19519
 
6.5%
n19497
 
6.5%
,17381
 
5.8%
r14354
 
4.8%
o14072
 
4.7%
;12296
 
4.1%
l9893
 
3.3%
Other values (52)111909
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)302105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34816
 
11.5%
a28585
 
9.5%
e19783
 
6.5%
i19519
 
6.5%
n19497
 
6.5%
,17381
 
5.8%
r14354
 
4.8%
o14072
 
4.7%
;12296
 
4.1%
l9893
 
3.3%
Other values (52)111909
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)302105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34816
 
11.5%
a28585
 
9.5%
e19783
 
6.5%
i19519
 
6.5%
n19497
 
6.5%
,17381
 
5.8%
r14354
 
4.8%
o14072
 
4.7%
;12296
 
4.1%
l9893
 
3.3%
Other values (52)111909
37.0%
Distinct21
Distinct (%)50.0%
Missing5100
Missing (%)99.2%
Memory size162.1 KiB
2026-01-14T11:01:23.422833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length22
Mean length14.261905
Min length6

Characters and Unicode

Total characters599
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)33.3%

Sample

1st rowAnonymous
2nd rowFonseca, D
3rd rowFelicia, P
4th rowFonseca, D
5th rowRos, M
ValueCountFrequency (%)
d12
 
10.4%
fonseca11
 
9.6%
m6
 
5.2%
ros4
 
3.5%
wang4
 
3.5%
ps4
 
3.5%
neumann3
 
2.6%
md3
 
2.6%
dion3
 
2.6%
l3
 
2.6%
Other values (49)62
53.9%
2026-01-14T11:01:23.990362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)599
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
73
 
12.2%
,57
 
9.5%
n40
 
6.7%
a36
 
6.0%
o35
 
5.8%
e34
 
5.7%
s24
 
4.0%
D21
 
3.5%
c19
 
3.2%
r19
 
3.2%
Other values (36)241
40.2%

Group Authors
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing5140
Missing (%)> 99.9%
Memory size160.9 KiB
2026-01-14T11:01:24.217842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length21
Mean length21
Min length18

Characters and Unicode

Total characters42
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowCRRAFT Partnership
2nd rowDGKL Working Grp Digital
ValueCountFrequency (%)
crraft1
16.7%
partnership1
16.7%
dgkl1
16.7%
working1
16.7%
grp1
16.7%
digital1
16.7%
2026-01-14T11:01:24.624233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r4
 
9.5%
4
 
9.5%
i4
 
9.5%
a2
 
4.8%
R2
 
4.8%
n2
 
4.8%
p2
 
4.8%
t2
 
4.8%
D2
 
4.8%
g2
 
4.8%
Other values (15)16
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r4
 
9.5%
4
 
9.5%
i4
 
9.5%
a2
 
4.8%
R2
 
4.8%
n2
 
4.8%
p2
 
4.8%
t2
 
4.8%
D2
 
4.8%
g2
 
4.8%
Other values (15)16
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r4
 
9.5%
4
 
9.5%
i4
 
9.5%
a2
 
4.8%
R2
 
4.8%
n2
 
4.8%
p2
 
4.8%
t2
 
4.8%
D2
 
4.8%
g2
 
4.8%
Other values (15)16
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r4
 
9.5%
4
 
9.5%
i4
 
9.5%
a2
 
4.8%
R2
 
4.8%
n2
 
4.8%
p2
 
4.8%
t2
 
4.8%
D2
 
4.8%
g2
 
4.8%
Other values (15)16
38.1%
Distinct5128
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size735.5 KiB
2026-01-14T11:01:25.051621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length242
Median length168
Mean length97.060288
Min length6

Characters and Unicode

Total characters499084
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5116 ?
Unique (%)99.5%

Sample

1st rowComputational Thinking Ability Training in College Computer Teaching
2nd rowPrinciples of Computational Thinking Tools
3rd rowIntegrating Computational Thinking into the Process of Learning Artificial Intelligence
4th rowThe Development and Challenges of Computational Thinking Board Games
5th rowThe Study on Computational Thinking
ValueCountFrequency (%)
computational2788
 
4.4%
thinking2760
 
4.3%
of2563
 
4.0%
in2469
 
3.9%
and2458
 
3.8%
a1896
 
3.0%
the1782
 
2.8%
for1302
 
2.0%
learning1095
 
1.7%
to1029
 
1.6%
Other values (5669)43834
68.5%
2026-01-14T11:01:25.911654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58834
 
11.8%
n38976
 
7.8%
i38340
 
7.7%
e34819
 
7.0%
o31969
 
6.4%
t31765
 
6.4%
a31147
 
6.2%
r21954
 
4.4%
s18140
 
3.6%
l16097
 
3.2%
Other values (81)177043
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)499084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
58834
 
11.8%
n38976
 
7.8%
i38340
 
7.7%
e34819
 
7.0%
o31969
 
6.4%
t31765
 
6.4%
a31147
 
6.2%
r21954
 
4.4%
s18140
 
3.6%
l16097
 
3.2%
Other values (81)177043
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)499084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
58834
 
11.8%
n38976
 
7.8%
i38340
 
7.7%
e34819
 
7.0%
o31969
 
6.4%
t31765
 
6.4%
a31147
 
6.2%
r21954
 
4.4%
s18140
 
3.6%
l16097
 
3.2%
Other values (81)177043
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)499084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
58834
 
11.8%
n38976
 
7.8%
i38340
 
7.7%
e34819
 
7.0%
o31969
 
6.4%
t31765
 
6.4%
a31147
 
6.2%
r21954
 
4.4%
s18140
 
3.6%
l16097
 
3.2%
Other values (81)177043
35.5%
Distinct1607
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Memory size528.7 KiB
2026-01-14T11:01:26.411972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length242
Median length125
Mean length56.26196
Min length2

Characters and Unicode

Total characters289299
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique975 ?
Unique (%)19.0%

Sample

1st rowPROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND TECHNOLOGY EDUCATION (ICSSTE 2015)
2nd rowEMERGING RESEARCH, PRACTICE, AND POLICY ON COMPUTATIONAL THINKING
3rd rowICEMT 2019: 2019 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND MULTIMEDIA TECHNOLOGY
4th row2018 FIRST INTERNATIONAL COGNITIVE CITIES CONFERENCE (IC3 2018)
5th row2012 INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MANAGEMENT INNOVATION (ERMI 2012), VOL 1
ValueCountFrequency (%)
education2506
 
6.6%
of1907
 
5.0%
and1826
 
4.8%
on1570
 
4.1%
conference1497
 
3.9%
in1256
 
3.3%
international1063
 
2.8%
the1020
 
2.7%
proceedings973
 
2.6%
science782
 
2.1%
Other values (1866)23510
62.0%
2026-01-14T11:01:26.934438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32768
11.3%
E29565
 
10.2%
N28186
 
9.7%
I22395
 
7.7%
O21399
 
7.4%
C19269
 
6.7%
A17467
 
6.0%
T17147
 
5.9%
R12556
 
4.3%
S10706
 
3.7%
Other values (64)77841
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)289299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32768
11.3%
E29565
 
10.2%
N28186
 
9.7%
I22395
 
7.7%
O21399
 
7.4%
C19269
 
6.7%
A17467
 
6.0%
T17147
 
5.9%
R12556
 
4.3%
S10706
 
3.7%
Other values (64)77841
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)289299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32768
11.3%
E29565
 
10.2%
N28186
 
9.7%
I22395
 
7.7%
O21399
 
7.4%
C19269
 
6.7%
A17467
 
6.0%
T17147
 
5.9%
R12556
 
4.3%
S10706
 
3.7%
Other values (64)77841
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)289299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32768
11.3%
E29565
 
10.2%
N28186
 
9.7%
I22395
 
7.7%
O21399
 
7.4%
C19269
 
6.7%
A17467
 
6.0%
T17147
 
5.9%
R12556
 
4.3%
S10706
 
3.7%
Other values (64)77841
26.9%

Book Series Title
Text

Missing 

Distinct164
Distinct (%)13.3%
Missing3911
Missing (%)76.1%
Memory size234.0 KiB
2026-01-14T11:01:27.282197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length108
Median length86
Mean length43.855402
Min length5

Characters and Unicode

Total characters53986
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)6.1%

Sample

1st rowAdvances in Social Science Education and Humanities Research
2nd rowEducational Communications and Technology-Issues and Innovations
3rd rowInternational Conference on Systems and Informatics
4th rowSymposium on Visual Languages and Human Centric Computing VL HCC
5th rowApplied Mechanics and Materials
ValueCountFrequency (%)
in535
 
8.0%
conference531
 
7.9%
and384
 
5.7%
education356
 
5.3%
on287
 
4.3%
science272
 
4.1%
proceedings256
 
3.8%
computer243
 
3.6%
notes221
 
3.3%
lecture221
 
3.3%
Other values (255)3381
50.6%
2026-01-14T11:01:27.830129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n6317
 
11.7%
e5928
 
11.0%
5456
 
10.1%
o3939
 
7.3%
i3647
 
6.8%
t2896
 
5.4%
c2795
 
5.2%
a2624
 
4.9%
r2592
 
4.8%
s1877
 
3.5%
Other values (50)15915
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)53986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n6317
 
11.7%
e5928
 
11.0%
5456
 
10.1%
o3939
 
7.3%
i3647
 
6.8%
t2896
 
5.4%
c2795
 
5.2%
a2624
 
4.9%
r2592
 
4.8%
s1877
 
3.5%
Other values (50)15915
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)53986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n6317
 
11.7%
e5928
 
11.0%
5456
 
10.1%
o3939
 
7.3%
i3647
 
6.8%
t2896
 
5.4%
c2795
 
5.2%
a2624
 
4.9%
r2592
 
4.8%
s1877
 
3.5%
Other values (50)15915
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)53986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n6317
 
11.7%
e5928
 
11.0%
5456
 
10.1%
o3939
 
7.3%
i3647
 
6.8%
t2896
 
5.4%
c2795
 
5.2%
a2624
 
4.9%
r2592
 
4.8%
s1877
 
3.5%
Other values (50)15915
29.5%

Book Series Subtitle
Unsupported

Missing  Rejected  Unsupported 

Missing5142
Missing (%)100.0%
Memory size40.3 KiB

Language
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
English
4964 
Spanish
 
117
Portuguese
 
32
Chinese
 
9
French
 
6
Other values (7)
 
14

Length

Max length11
Median length7
Mean length7.0173084
Min length6

Characters and Unicode

Total characters36083
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English4964
96.5%
Spanish117
 
2.3%
Portuguese32
 
0.6%
Chinese9
 
0.2%
French6
 
0.1%
Turkish3
 
0.1%
German3
 
0.1%
Korean2
 
< 0.1%
Russian2
 
< 0.1%
Arabic2
 
< 0.1%
Other values (2)2
 
< 0.1%

Length

2026-01-14T11:01:27.961484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english4964
96.5%
spanish117
 
2.3%
portuguese32
 
0.6%
chinese9
 
0.2%
french6
 
0.1%
turkish3
 
0.1%
german3
 
0.1%
korean2
 
< 0.1%
russian2
 
< 0.1%
arabic2
 
< 0.1%
Other values (2)2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s5130
14.2%
n5106
14.2%
i5101
14.1%
h5099
14.1%
g4996
13.8%
E4964
13.8%
l4964
13.8%
a128
 
0.4%
p118
 
0.3%
S117
 
0.3%
Other values (20)360
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)36083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s5130
14.2%
n5106
14.2%
i5101
14.1%
h5099
14.1%
g4996
13.8%
E4964
13.8%
l4964
13.8%
a128
 
0.4%
p118
 
0.3%
S117
 
0.3%
Other values (20)360
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s5130
14.2%
n5106
14.2%
i5101
14.1%
h5099
14.1%
g4996
13.8%
E4964
13.8%
l4964
13.8%
a128
 
0.4%
p118
 
0.3%
S117
 
0.3%
Other values (20)360
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s5130
14.2%
n5106
14.2%
i5101
14.1%
h5099
14.1%
g4996
13.8%
E4964
13.8%
l4964
13.8%
a128
 
0.4%
p118
 
0.3%
S117
 
0.3%
Other values (20)360
 
1.0%

Document Type
Categorical

High correlation  Imbalance 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size308.2 KiB
Proceedings Paper
2349 
Article
2258 
Review
 
196
Article; Early Access
 
95
Article; Book Chapter
 
90
Other values (14)
 
154

Length

Max length32
Median length30
Mean length12.345974
Min length4

Characters and Unicode

Total characters63483
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowProceedings Paper
2nd rowArticle; Book Chapter
3rd rowProceedings Paper
4th rowProceedings Paper
5th rowProceedings Paper

Common Values

ValueCountFrequency (%)
Proceedings Paper2349
45.7%
Article2258
43.9%
Review196
 
3.8%
Article; Early Access95
 
1.8%
Article; Book Chapter90
 
1.8%
Editorial Material56
 
1.1%
Meeting Abstract29
 
0.6%
Article; Proceedings Paper15
 
0.3%
Correction13
 
0.3%
Book Review9
 
0.2%
Other values (9)32
 
0.6%

Length

2026-01-14T11:01:28.089605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
article2459
30.6%
proceedings2364
29.4%
paper2364
29.4%
review214
 
2.7%
book113
 
1.4%
early102
 
1.3%
access102
 
1.3%
chapter101
 
1.3%
editorial66
 
0.8%
material66
 
0.8%
Other values (8)83
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e10339
16.3%
r7588
12.0%
i5280
 
8.3%
c5071
 
8.0%
P4729
 
7.4%
2892
 
4.6%
t2813
 
4.4%
a2797
 
4.4%
l2694
 
4.2%
o2684
 
4.2%
Other values (22)16596
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)63483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10339
16.3%
r7588
12.0%
i5280
 
8.3%
c5071
 
8.0%
P4729
 
7.4%
2892
 
4.6%
t2813
 
4.4%
a2797
 
4.4%
l2694
 
4.2%
o2684
 
4.2%
Other values (22)16596
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10339
16.3%
r7588
12.0%
i5280
 
8.3%
c5071
 
8.0%
P4729
 
7.4%
2892
 
4.6%
t2813
 
4.4%
a2797
 
4.4%
l2694
 
4.2%
o2684
 
4.2%
Other values (22)16596
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10339
16.3%
r7588
12.0%
i5280
 
8.3%
c5071
 
8.0%
P4729
 
7.4%
2892
 
4.6%
t2813
 
4.4%
a2797
 
4.4%
l2694
 
4.2%
o2684
 
4.2%
Other values (22)16596
26.1%

Conference Title
Text

Missing 

Distinct916
Distinct (%)38.3%
Missing2751
Missing (%)53.5%
Memory size375.5 KiB
2026-01-14T11:01:28.420677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length239
Median length168
Mean length74.922208
Min length12

Characters and Unicode

Total characters179139
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique583 ?
Unique (%)24.4%

Sample

1st rowInternational Conference on Social Science and Technology Education (ICSSTE)
2nd row3rd International Conference on Education and Multimedia Technology (ICEMT)
3rd row1st International Cognitive Cities Conference (IC3)
4th rowInternational Conference on Education Reform and Management Innovation (ERMI 2012)
5th row15th International Conference on Education Technology and Computers (ICETC)
ValueCountFrequency (%)
conference1920
 
8.6%
on1888
 
8.4%
education1320
 
5.9%
international1222
 
5.5%
and1124
 
5.0%
in686
 
3.1%
computer483
 
2.2%
science467
 
2.1%
ieee438
 
2.0%
symposium385
 
1.7%
Other values (1245)12465
55.7%
2026-01-14T11:01:28.997044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n20359
 
11.4%
20007
 
11.2%
e14440
 
8.1%
o12448
 
6.9%
t9789
 
5.5%
i9669
 
5.4%
a9313
 
5.2%
c7231
 
4.0%
r7127
 
4.0%
C5723
 
3.2%
Other values (63)63033
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)179139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n20359
 
11.4%
20007
 
11.2%
e14440
 
8.1%
o12448
 
6.9%
t9789
 
5.5%
i9669
 
5.4%
a9313
 
5.2%
c7231
 
4.0%
r7127
 
4.0%
C5723
 
3.2%
Other values (63)63033
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)179139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n20359
 
11.4%
20007
 
11.2%
e14440
 
8.1%
o12448
 
6.9%
t9789
 
5.5%
i9669
 
5.4%
a9313
 
5.2%
c7231
 
4.0%
r7127
 
4.0%
C5723
 
3.2%
Other values (63)63033
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)179139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n20359
 
11.4%
20007
 
11.2%
e14440
 
8.1%
o12448
 
6.9%
t9789
 
5.5%
i9669
 
5.4%
a9313
 
5.2%
c7231
 
4.0%
r7127
 
4.0%
C5723
 
3.2%
Other values (63)63033
35.2%

Conference Date
Text

Missing 

Distinct882
Distinct (%)36.9%
Missing2751
Missing (%)53.5%
Memory size236.1 KiB
2026-01-14T11:01:29.524902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length15
Mean length15.220828
Min length4

Characters and Unicode

Total characters36393
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique512 ?
Unique (%)21.4%

Sample

1st rowAPR 11-12, 2015
2nd rowJUL 22-25, 2019
3rd rowAUG 07-09, 2018
4th rowDEC 04-05, 2012
5th rowSEP 26-28, 2023
ValueCountFrequency (%)
oct405
 
5.5%
2019317
 
4.3%
jul315
 
4.3%
mar315
 
4.3%
2018275
 
3.7%
nov241
 
3.3%
2017240
 
3.3%
2021239
 
3.3%
jun235
 
3.2%
2020229
 
3.1%
Other values (230)4527
61.7%
2026-01-14T11:01:30.157738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25750
15.8%
4947
13.6%
04409
12.1%
13790
 
10.4%
,2382
 
6.5%
-2344
 
6.4%
3891
 
2.4%
A840
 
2.3%
8813
 
2.2%
U736
 
2.0%
Other values (22)9491
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)36393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25750
15.8%
4947
13.6%
04409
12.1%
13790
 
10.4%
,2382
 
6.5%
-2344
 
6.4%
3891
 
2.4%
A840
 
2.3%
8813
 
2.2%
U736
 
2.0%
Other values (22)9491
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25750
15.8%
4947
13.6%
04409
12.1%
13790
 
10.4%
,2382
 
6.5%
-2344
 
6.4%
3891
 
2.4%
A840
 
2.3%
8813
 
2.2%
U736
 
2.0%
Other values (22)9491
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25750
15.8%
4947
13.6%
04409
12.1%
13790
 
10.4%
,2382
 
6.5%
-2344
 
6.4%
3891
 
2.4%
A840
 
2.3%
8813
 
2.2%
U736
 
2.0%
Other values (22)9491
26.1%

Conference Location
Text

Missing 

Distinct565
Distinct (%)23.6%
Missing2751
Missing (%)53.5%
Memory size248.0 KiB
2026-01-14T11:01:30.642346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length94
Median length76
Mean length20.333752
Min length2

Characters and Unicode

Total characters48618
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)11.4%

Sample

1st rowSanya, PEOPLES R CHINA
2nd rowNagoya, JAPAN
3rd rowOkinawa, JAPAN
4th rowShenzhen, PEOPLES R CHINA
5th rowUniv Barcelona, Barcelona, SPAIN
ValueCountFrequency (%)
univ429
 
5.9%
network272
 
3.8%
electr272
 
3.8%
spain219
 
3.0%
hong127
 
1.8%
kong127
 
1.8%
china122
 
1.7%
r112
 
1.6%
peoples112
 
1.6%
england73
 
1.0%
Other values (817)5354
74.2%
2026-01-14T11:01:31.239195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4828
 
9.9%
n2706
 
5.6%
,2630
 
5.4%
a2546
 
5.2%
A2192
 
4.5%
i2018
 
4.2%
E1959
 
4.0%
N1890
 
3.9%
e1823
 
3.7%
o1815
 
3.7%
Other values (48)24211
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)48618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4828
 
9.9%
n2706
 
5.6%
,2630
 
5.4%
a2546
 
5.2%
A2192
 
4.5%
i2018
 
4.2%
E1959
 
4.0%
N1890
 
3.9%
e1823
 
3.7%
o1815
 
3.7%
Other values (48)24211
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4828
 
9.9%
n2706
 
5.6%
,2630
 
5.4%
a2546
 
5.2%
A2192
 
4.5%
i2018
 
4.2%
E1959
 
4.0%
N1890
 
3.9%
e1823
 
3.7%
o1815
 
3.7%
Other values (48)24211
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4828
 
9.9%
n2706
 
5.6%
,2630
 
5.4%
a2546
 
5.2%
A2192
 
4.5%
i2018
 
4.2%
E1959
 
4.0%
N1890
 
3.9%
e1823
 
3.7%
o1815
 
3.7%
Other values (48)24211
49.8%

Conference Sponsor
Text

Missing 

Distinct531
Distinct (%)31.4%
Missing3453
Missing (%)67.2%
Memory size328.7 KiB
2026-01-14T11:01:31.499419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1103
Median length272
Mean length84.797513
Min length3

Characters and Unicode

Total characters143223
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)18.8%

Sample

1st rowInt Assoc Cyber Sci & Engn
2nd rowIEEE Comp Soc,Okinawa Inst Sci & Technol
3rd rowGrup Recerca Ensenyament Aprenentatge Virtual,Univ Warwick,Tecnologico Monterrey
4th rowAssoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Comp Sci Educ,ACM Europe Council,Informat Europe,Github Educ,Univ Milano, Dipartimento Informatica
5th rowShanghai Dianji Univ, Sch Elect & Informat,IEEE Syst, Man, & Cybernet Soc
ValueCountFrequency (%)
comp1331
 
7.4%
educ730
 
4.1%
707
 
3.9%
sci507
 
2.8%
univ475
 
2.6%
assoc474
 
2.6%
soc380
 
2.1%
grp345
 
1.9%
special317
 
1.8%
res317
 
1.8%
Other values (2441)12358
68.9%
2026-01-14T11:01:31.951316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16252
 
11.3%
n8707
 
6.1%
e8403
 
5.9%
o7992
 
5.6%
i7529
 
5.3%
c7406
 
5.2%
a7027
 
4.9%
E6306
 
4.4%
,6097
 
4.3%
t5726
 
4.0%
Other values (61)61778
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)143223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16252
 
11.3%
n8707
 
6.1%
e8403
 
5.9%
o7992
 
5.6%
i7529
 
5.3%
c7406
 
5.2%
a7027
 
4.9%
E6306
 
4.4%
,6097
 
4.3%
t5726
 
4.0%
Other values (61)61778
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)143223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16252
 
11.3%
n8707
 
6.1%
e8403
 
5.9%
o7992
 
5.6%
i7529
 
5.3%
c7406
 
5.2%
a7027
 
4.9%
E6306
 
4.4%
,6097
 
4.3%
t5726
 
4.0%
Other values (61)61778
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)143223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16252
 
11.3%
n8707
 
6.1%
e8403
 
5.9%
o7992
 
5.6%
i7529
 
5.3%
c7406
 
5.2%
a7027
 
4.9%
E6306
 
4.4%
,6097
 
4.3%
t5726
 
4.0%
Other values (61)61778
43.1%

Conference Host
Text

Missing 

Distinct198
Distinct (%)37.9%
Missing4619
Missing (%)89.8%
Memory size180.7 KiB
2026-01-14T11:01:32.369277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length74
Median length48
Mean length21.839388
Min length4

Characters and Unicode

Total characters11422
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)21.4%

Sample

1st rowUniv Barcelona
2nd rowUniv Milano
3rd rowTU Wien
4th rowUniv Turku
5th rowCity Univ Hong Kong
ValueCountFrequency (%)
univ429
 
22.5%
technol46
 
2.4%
sci45
 
2.4%
educ45
 
2.4%
kong40
 
2.1%
hong40
 
2.1%
city35
 
1.8%
35
 
1.8%
fac27
 
1.4%
inst27
 
1.4%
Other values (338)1135
59.6%
2026-01-14T11:01:32.963869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1381
 
12.1%
n1223
 
10.7%
i966
 
8.5%
a655
 
5.7%
e628
 
5.5%
o550
 
4.8%
t483
 
4.2%
v467
 
4.1%
U452
 
4.0%
r390
 
3.4%
Other values (46)4227
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)11422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1381
 
12.1%
n1223
 
10.7%
i966
 
8.5%
a655
 
5.7%
e628
 
5.5%
o550
 
4.8%
t483
 
4.2%
v467
 
4.1%
U452
 
4.0%
r390
 
3.4%
Other values (46)4227
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1381
 
12.1%
n1223
 
10.7%
i966
 
8.5%
a655
 
5.7%
e628
 
5.5%
o550
 
4.8%
t483
 
4.2%
v467
 
4.1%
U452
 
4.0%
r390
 
3.4%
Other values (46)4227
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1381
 
12.1%
n1223
 
10.7%
i966
 
8.5%
a655
 
5.7%
e628
 
5.5%
o550
 
4.8%
t483
 
4.2%
v467
 
4.1%
U452
 
4.0%
r390
 
3.4%
Other values (46)4227
37.0%

Author Keywords
Text

Missing 

Distinct4644
Distinct (%)99.1%
Missing457
Missing (%)8.9%
Memory size682.9 KiB
2026-01-14T11:01:33.321953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length871
Median length216
Mean length96.636286
Min length3

Characters and Unicode

Total characters452741
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4609 ?
Unique (%)98.4%

Sample

1st rowComputational Thinking; Computer Teaching; Blended Teaching Model
2nd rowComputational Thinking Process; Three stages of the Computational Thinking Process; Computational Thinking Tools; Principles of Computational Thinking Tools
3rd rowComputational thinking; Experiential learning; Artificial intelligence
4th rowcomputational thinking; board game; coding; game-based learning
5th rowinformation process; computational thinking; talent-training strategy
ValueCountFrequency (%)
thinking3724
 
8.1%
computational3617
 
7.9%
education2300
 
5.0%
learning1582
 
3.4%
programming1408
 
3.1%
science776
 
1.7%
computer706
 
1.5%
educational478
 
1.0%
design445
 
1.0%
robotics396
 
0.9%
Other values (4045)30429
66.4%
2026-01-14T11:01:33.845223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41176
 
9.1%
i39793
 
8.8%
n36467
 
8.1%
t32265
 
7.1%
e31673
 
7.0%
a31628
 
7.0%
o29098
 
6.4%
r19823
 
4.4%
c18925
 
4.2%
;18830
 
4.2%
Other values (71)153063
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)452741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41176
 
9.1%
i39793
 
8.8%
n36467
 
8.1%
t32265
 
7.1%
e31673
 
7.0%
a31628
 
7.0%
o29098
 
6.4%
r19823
 
4.4%
c18925
 
4.2%
;18830
 
4.2%
Other values (71)153063
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)452741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41176
 
9.1%
i39793
 
8.8%
n36467
 
8.1%
t32265
 
7.1%
e31673
 
7.0%
a31628
 
7.0%
o29098
 
6.4%
r19823
 
4.4%
c18925
 
4.2%
;18830
 
4.2%
Other values (71)153063
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)452741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41176
 
9.1%
i39793
 
8.8%
n36467
 
8.1%
t32265
 
7.1%
e31673
 
7.0%
a31628
 
7.0%
o29098
 
6.4%
r19823
 
4.4%
c18925
 
4.2%
;18830
 
4.2%
Other values (71)153063
33.8%

Keywords Plus
Text

Missing 

Distinct2145
Distinct (%)77.3%
Missing2366
Missing (%)46.0%
Memory size332.0 KiB
2026-01-14T11:01:34.123434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length187
Median length144
Mean length46.144452
Min length2

Characters and Unicode

Total characters128097
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2008 ?
Unique (%)72.3%

Sample

1st rowEND
2nd rowELEMENTARY CLASSROOMS; COMPUTER-SCIENCE; ROBOTICS; SKILLS; PAPER; EXPLORATION; VIEWPOINT; EDUCATION; VALIDITY; DESIGN
3rd rowYOUTH; CURRICULUM
4th rowVALIDITY; K-12
5th rowMATHEMATICS; LANGUAGE
ValueCountFrequency (%)
thinking921
 
7.3%
computational820
 
6.5%
education470
 
3.7%
students352
 
2.8%
science343
 
2.7%
robotics327
 
2.6%
k-12311
 
2.5%
design304
 
2.4%
skills300
 
2.4%
mathematics220
 
1.7%
Other values (1449)8241
65.4%
2026-01-14T11:01:34.593604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E11315
 
8.8%
I10819
 
8.4%
T10346
 
8.1%
9833
 
7.7%
N9272
 
7.2%
A8032
 
6.3%
O7997
 
6.2%
;7636
 
6.0%
C7070
 
5.5%
S6921
 
5.4%
Other values (30)38856
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)128097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E11315
 
8.8%
I10819
 
8.4%
T10346
 
8.1%
9833
 
7.7%
N9272
 
7.2%
A8032
 
6.3%
O7997
 
6.2%
;7636
 
6.0%
C7070
 
5.5%
S6921
 
5.4%
Other values (30)38856
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)128097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E11315
 
8.8%
I10819
 
8.4%
T10346
 
8.1%
9833
 
7.7%
N9272
 
7.2%
A8032
 
6.3%
O7997
 
6.2%
;7636
 
6.0%
C7070
 
5.5%
S6921
 
5.4%
Other values (30)38856
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)128097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E11315
 
8.8%
I10819
 
8.4%
T10346
 
8.1%
9833
 
7.7%
N9272
 
7.2%
A8032
 
6.3%
O7997
 
6.2%
;7636
 
6.0%
C7070
 
5.5%
S6921
 
5.4%
Other values (30)38856
30.3%

Abstract
Text

Missing 

Distinct4971
Distinct (%)99.9%
Missing164
Missing (%)3.2%
Memory size6.3 MiB
2026-01-14T11:01:34.975224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22855
Median length1782
Mean length1283.3435
Min length81

Characters and Unicode

Total characters6388484
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4964 ?
Unique (%)99.7%

Sample

1st rowComputational thinking is ubiquitous, and has become a hot spot in education. As the core mission of college computer teaching, computational thinking training naturally causes widespread concern in the field of basic computer education. In this study, we propose the computational thinking formation procedural model, which reveals that computational thinking is the unity of internal and process, and the computational thinking ability training process is an important reason for the formation of computational thinking ability. And based on the factors of computational thinking training process, we construct the computational thinking - based blended teaching model.
2nd rowComputational Thinking is a fundamental skill for the twenty-first century workforce. This broad target audience, including teachers and students with no programming experience, necessitates a shift in perspective toward Computational Thinking Tools that not only provide highly accessible programming environments but explicitly support the Computational Thinking Process. This evolution is crucial if Computational Thinking Tools are to be relevant to a wide range of school disciplines including STEM, art, music, and language learning. Computational Thinking Tools must help users through three fundamental stages of Computational Thinking: problem formulation, solution expression, and execution/evaluation. This chapter outlines three principles, and employs AgentCubes online as an example, on how a Computational Thinking Tool provides support for these stages by unifying human abilities with computer affordances.
3rd rowIn recent years, computational thinking has once again received attention widely. Computational thinking is generally considered to be the ability to be acquired. However, this study is to use computational thinking as part of the learning method. In order to explore the application of computational thinking in teaching, this study first collected the main review papers, as well as the literature on the assessment of computational thinking, and examined their views. Then, this study proposes a learning method that integrates computational thinking into experiential learning theory and applies it to learning artificial intelligence techniques.
4th rowThe promotion of computational thinking education has become a worldwide trend. To cultivate the computational thinking ability of children at young age, many computational thinking board games have appeared recently. This article introduces five computational thinking board games, including Robot Turtles, King of Pirates, Doggy Code, ROBOT WARS Coding Board Game, and Code master, and then to analyze its characteristics respectively. Additionally, this article also points out the current limitations and challenges of computational thinking board games. We hope more schools or operators will join the development of computational thinking education in the future.
5th rowThis article makes an introduction to the research of computational thinking on its form, characteristic and present situation, analyses the background and the reasons for the rise of computational thinking, draws the conclusion that computational thinking should be considered from national strategic level of talent-training, and proposes some countermeasures of computational thinking enhancement, talent-training and information process.
ValueCountFrequency (%)
the51301
 
5.6%
and36949
 
4.0%
of32708
 
3.6%
to25008
 
2.7%
in24936
 
2.7%
a17824
 
2.0%
students10328
 
1.1%
for9945
 
1.1%
thinking9300
 
1.0%
this8956
 
1.0%
Other values (24207)686033
75.1%
2026-01-14T11:01:35.579861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
908321
14.2%
e616165
 
9.6%
t477621
 
7.5%
i440803
 
6.9%
n421788
 
6.6%
a403329
 
6.3%
o377772
 
5.9%
s354076
 
5.5%
r312893
 
4.9%
c225762
 
3.5%
Other values (82)1849954
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6388484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
908321
14.2%
e616165
 
9.6%
t477621
 
7.5%
i440803
 
6.9%
n421788
 
6.6%
a403329
 
6.3%
o377772
 
5.9%
s354076
 
5.5%
r312893
 
4.9%
c225762
 
3.5%
Other values (82)1849954
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6388484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
908321
14.2%
e616165
 
9.6%
t477621
 
7.5%
i440803
 
6.9%
n421788
 
6.6%
a403329
 
6.3%
o377772
 
5.9%
s354076
 
5.5%
r312893
 
4.9%
c225762
 
3.5%
Other values (82)1849954
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6388484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
908321
14.2%
e616165
 
9.6%
t477621
 
7.5%
i440803
 
6.9%
n421788
 
6.6%
a403329
 
6.3%
o377772
 
5.9%
s354076
 
5.5%
r312893
 
4.9%
c225762
 
3.5%
Other values (82)1849954
29.0%
Distinct5014
Distinct (%)98.1%
Missing30
Missing (%)0.6%
Memory size1.2 MiB
2026-01-14T11:01:36.319413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1245
Median length485
Mean length193.00528
Min length28

Characters and Unicode

Total characters986643
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4933 ?
Unique (%)96.5%

Sample

1st rowYancheng Inst Ind Technol, Yancheng 224001, Peoples R China
2nd row[Repenning, Alexander; Escherle, Nora A.] Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland; [Basawapatna, Ashok R.] SUNY Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USA
3rd row[Shih, Wen-Chung] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan
4th row[Wu, Sheng-Yi] Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan
5th row[Lu Changjin; Zhang Shuai; Chen Xiuqiong] Sanming Univ, Dept Math & Comp Sci, Sanming, Fujian Province, Peoples R China
ValueCountFrequency (%)
univ8498
 
5.9%
educ3383
 
2.4%
3247
 
2.3%
usa2964
 
2.1%
dept2933
 
2.0%
sci2181
 
1.5%
technol1594
 
1.1%
comp1395
 
1.0%
china1368
 
1.0%
sch1365
 
0.9%
Other values (20985)114810
79.9%
2026-01-14T11:01:38.219845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138626
 
14.1%
a72666
 
7.4%
n63274
 
6.4%
i56418
 
5.7%
e54238
 
5.5%
,51159
 
5.2%
o40778
 
4.1%
r35440
 
3.6%
l31065
 
3.1%
t29340
 
3.0%
Other values (64)413639
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)986643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
138626
 
14.1%
a72666
 
7.4%
n63274
 
6.4%
i56418
 
5.7%
e54238
 
5.5%
,51159
 
5.2%
o40778
 
4.1%
r35440
 
3.6%
l31065
 
3.1%
t29340
 
3.0%
Other values (64)413639
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)986643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
138626
 
14.1%
a72666
 
7.4%
n63274
 
6.4%
i56418
 
5.7%
e54238
 
5.5%
,51159
 
5.2%
o40778
 
4.1%
r35440
 
3.6%
l31065
 
3.1%
t29340
 
3.0%
Other values (64)413639
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)986643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
138626
 
14.1%
a72666
 
7.4%
n63274
 
6.4%
i56418
 
5.7%
e54238
 
5.5%
,51159
 
5.2%
o40778
 
4.1%
r35440
 
3.6%
l31065
 
3.1%
t29340
 
3.0%
Other values (64)413639
41.9%

Affiliations
Text

Missing 

Distinct2946
Distinct (%)60.7%
Missing290
Missing (%)5.6%
Memory size553.5 KiB
2026-01-14T11:01:39.141138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length725
Median length277
Mean length65.883141
Min length5

Characters and Unicode

Total characters319665
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2239 ?
Unique (%)46.1%

Sample

1st rowFHNW University of Applied Sciences & Arts Northwestern Switzerland; State University of New York (SUNY) System; SUNY Old Westbury
2nd rowAsia University Taiwan
3rd rowNational Pingtung University
4th rowSanming University
5th rowCentral China Normal University; Zhongnan University of Economics & Law
ValueCountFrequency (%)
university7968
 
19.6%
of4565
 
11.2%
system1195
 
2.9%
de1011
 
2.5%
state863
 
2.1%
universidad646
 
1.6%
technology615
 
1.5%
national538
 
1.3%
511
 
1.3%
normal427
 
1.1%
Other values (2213)22277
54.8%
2026-01-14T11:01:42.047676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35764
 
11.2%
i31015
 
9.7%
e25491
 
8.0%
n23036
 
7.2%
a19425
 
6.1%
t19410
 
6.1%
r17039
 
5.3%
o16905
 
5.3%
s16473
 
5.2%
y11380
 
3.6%
Other values (57)103727
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)319665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35764
 
11.2%
i31015
 
9.7%
e25491
 
8.0%
n23036
 
7.2%
a19425
 
6.1%
t19410
 
6.1%
r17039
 
5.3%
o16905
 
5.3%
s16473
 
5.2%
y11380
 
3.6%
Other values (57)103727
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)319665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35764
 
11.2%
i31015
 
9.7%
e25491
 
8.0%
n23036
 
7.2%
a19425
 
6.1%
t19410
 
6.1%
r17039
 
5.3%
o16905
 
5.3%
s16473
 
5.2%
y11380
 
3.6%
Other values (57)103727
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)319665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35764
 
11.2%
i31015
 
9.7%
e25491
 
8.0%
n23036
 
7.2%
a19425
 
6.1%
t19410
 
6.1%
r17039
 
5.3%
o16905
 
5.3%
s16473
 
5.2%
y11380
 
3.6%
Other values (57)103727
32.4%

Reprint Addresses
Text

Missing 

Distinct4614
Distinct (%)91.4%
Missing92
Missing (%)1.8%
Memory size825.3 KiB
2026-01-14T11:01:42.972691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length457
Median length309
Mean length108.82515
Min length53

Characters and Unicode

Total characters549567
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4297 ?
Unique (%)85.1%

Sample

1st rowWeiguoZou (autor correspondiente), Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China.
2nd rowRepenning, A (autor correspondiente), Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland.
3rd rowShih, WC (autor correspondiente), Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.
4th rowWu, SY (autor correspondiente), Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan.
5th rowSong, LL (autor correspondiente), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
ValueCountFrequency (%)
autor5466
 
7.1%
correspondiente5466
 
7.1%
univ4623
 
6.0%
educ1854
 
2.4%
1746
 
2.3%
dept1583
 
2.1%
usa1334
 
1.7%
sci1178
 
1.5%
technol894
 
1.2%
r860
 
1.1%
Other values (9706)52044
67.5%
2026-01-14T11:01:43.969217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71998
 
13.1%
e37359
 
6.8%
n36595
 
6.7%
o32759
 
6.0%
a32347
 
5.9%
r29615
 
5.4%
,28712
 
5.2%
i27120
 
4.9%
t23983
 
4.4%
c15332
 
2.8%
Other values (80)213747
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)549567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
71998
 
13.1%
e37359
 
6.8%
n36595
 
6.7%
o32759
 
6.0%
a32347
 
5.9%
r29615
 
5.4%
,28712
 
5.2%
i27120
 
4.9%
t23983
 
4.4%
c15332
 
2.8%
Other values (80)213747
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)549567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
71998
 
13.1%
e37359
 
6.8%
n36595
 
6.7%
o32759
 
6.0%
a32347
 
5.9%
r29615
 
5.4%
,28712
 
5.2%
i27120
 
4.9%
t23983
 
4.4%
c15332
 
2.8%
Other values (80)213747
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)549567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
71998
 
13.1%
e37359
 
6.8%
n36595
 
6.7%
o32759
 
6.0%
a32347
 
5.9%
r29615
 
5.4%
,28712
 
5.2%
i27120
 
4.9%
t23983
 
4.4%
c15332
 
2.8%
Other values (80)213747
38.9%

Email Addresses
Text

Missing 

Distinct4153
Distinct (%)88.6%
Missing454
Missing (%)8.8%
Memory size491.5 KiB
2026-01-14T11:01:44.364018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length726
Median length224
Mean length55.236561
Min length10

Characters and Unicode

Total characters258949
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3808 ?
Unique (%)81.2%

Sample

1st rowalexander.repenning@fhnw.ch; basawapatnaa@oldwestbury.edu; nora.escherle@fhnw.ch
2nd rowwjshih@asia.edu.tw
3rd rowdigschool@gmail.com
4th rowsmlcj123@163.com
5th rowzwccnu@ccnu.edu.cn; linglingsong@mails.ccnu.edu.cn; hxj168168@zuel.edu.cn; wlqyy_fam@163.com
ValueCountFrequency (%)
sckong@eduhk.hk28
 
0.2%
grex@gsyc.urjc.es27
 
0.2%
mroman@edu.uned.es26
 
0.2%
dmfranklin@uchicago.edu23
 
0.2%
fgarcia@usal.es21
 
0.2%
jesus.moreno@programamos.es20
 
0.2%
bwmott@ncsu.edu19
 
0.2%
ayadav@msu.edu19
 
0.2%
misrael@coe.ufl.edu18
 
0.2%
wongkwg@hku.hk17
 
0.1%
Other values (7914)11512
98.1%
2026-01-14T11:01:45.108707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.20964
 
8.1%
e19980
 
7.7%
a19952
 
7.7%
u16960
 
6.5%
i14648
 
5.7%
n13614
 
5.3%
@11731
 
4.5%
o11267
 
4.4%
c11123
 
4.3%
r10954
 
4.2%
Other values (59)107756
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)258949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.20964
 
8.1%
e19980
 
7.7%
a19952
 
7.7%
u16960
 
6.5%
i14648
 
5.7%
n13614
 
5.3%
@11731
 
4.5%
o11267
 
4.4%
c11123
 
4.3%
r10954
 
4.2%
Other values (59)107756
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)258949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.20964
 
8.1%
e19980
 
7.7%
a19952
 
7.7%
u16960
 
6.5%
i14648
 
5.7%
n13614
 
5.3%
@11731
 
4.5%
o11267
 
4.4%
c11123
 
4.3%
r10954
 
4.2%
Other values (59)107756
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)258949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.20964
 
8.1%
e19980
 
7.7%
a19952
 
7.7%
u16960
 
6.5%
i14648
 
5.7%
n13614
 
5.3%
@11731
 
4.5%
o11267
 
4.4%
c11123
 
4.3%
r10954
 
4.2%
Other values (59)107756
41.6%

Researcher Ids
Text

Missing 

Distinct2728
Distinct (%)81.5%
Missing1794
Missing (%)34.9%
Memory size463.6 KiB
2026-01-14T11:01:46.694593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length503
Median length201
Mean length53.68399
Min length18

Characters and Unicode

Total characters179734
Distinct characters155
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2367 ?
Unique (%)70.7%

Sample

1st rowWu, Sheng-Yi/C-4143-2011
2nd rowLeung, Jessica Shuk Ching/G-4619-2013; Ezeamuzie, Ndudi Okechukwu/ABG-1289-2021
3rd rowBower, Matt/J-7574-2016; Arguel, Amaël/H-4424-2019; Boom, Kay-Dennis/R-5200-2018
4th rowdeng, wenbo/AAI-3947-2020
5th rowLiu, Shuai/P-3939-2017; Srivastava, Gautam/N-5668-2019
ValueCountFrequency (%)
758
 
5.5%
chen50
 
0.4%
li48
 
0.3%
de42
 
0.3%
wong40
 
0.3%
wang40
 
0.3%
liu39
 
0.3%
yang38
 
0.3%
hsu36
 
0.3%
marcos/c-5705-201333
 
0.2%
Other values (5933)12617
91.8%
2026-01-14T11:01:47.393421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212812
 
7.1%
-12632
 
7.0%
10393
 
5.8%
a9671
 
5.4%
08919
 
5.0%
e6425
 
3.6%
n6140
 
3.4%
i6132
 
3.4%
/5888
 
3.3%
,5887
 
3.3%
Other values (145)94835
52.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)179734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
212812
 
7.1%
-12632
 
7.0%
10393
 
5.8%
a9671
 
5.4%
08919
 
5.0%
e6425
 
3.6%
n6140
 
3.4%
i6132
 
3.4%
/5888
 
3.3%
,5887
 
3.3%
Other values (145)94835
52.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)179734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
212812
 
7.1%
-12632
 
7.0%
10393
 
5.8%
a9671
 
5.4%
08919
 
5.0%
e6425
 
3.6%
n6140
 
3.4%
i6132
 
3.4%
/5888
 
3.3%
,5887
 
3.3%
Other values (145)94835
52.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)179734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
212812
 
7.1%
-12632
 
7.0%
10393
 
5.8%
a9671
 
5.4%
08919
 
5.0%
e6425
 
3.6%
n6140
 
3.4%
i6132
 
3.4%
/5888
 
3.3%
,5887
 
3.3%
Other values (145)94835
52.8%

ORCIDs
Text

Missing 

Distinct2942
Distinct (%)87.4%
Missing1775
Missing (%)34.5%
Memory size544.3 KiB
2026-01-14T11:01:47.773095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length438
Median length238
Mean length73.659341
Min length24

Characters and Unicode

Total characters248011
Distinct characters164
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2662 ?
Unique (%)79.1%

Sample

1st rowREPENNING, ALEXANDER/0000-0002-2165-7533
2nd rowShih, Wen-Chung/0000-0003-4838-8473
3rd rowWu, Sheng-Yi/0000-0003-3022-1843
4th rowBenke, Gertraud/0000-0002-6710-191X
5th rowREPENNING, ALEXANDER/0000-0002-2165-7533
ValueCountFrequency (%)
64
 
0.4%
lee57
 
0.4%
li52
 
0.3%
liu50
 
0.3%
yang46
 
0.3%
chen44
 
0.3%
sun43
 
0.3%
wong40
 
0.3%
wang39
 
0.3%
de39
 
0.3%
Other values (7635)14622
96.9%
2026-01-14T11:01:48.476002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
051465
20.8%
-20684
 
8.3%
11729
 
4.7%
a10762
 
4.3%
28446
 
3.4%
e7591
 
3.1%
i7104
 
2.9%
n7091
 
2.9%
16945
 
2.8%
/6631
 
2.7%
Other values (154)109563
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)248011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
051465
20.8%
-20684
 
8.3%
11729
 
4.7%
a10762
 
4.3%
28446
 
3.4%
e7591
 
3.1%
i7104
 
2.9%
n7091
 
2.9%
16945
 
2.8%
/6631
 
2.7%
Other values (154)109563
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)248011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
051465
20.8%
-20684
 
8.3%
11729
 
4.7%
a10762
 
4.3%
28446
 
3.4%
e7591
 
3.1%
i7104
 
2.9%
n7091
 
2.9%
16945
 
2.8%
/6631
 
2.7%
Other values (154)109563
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)248011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
051465
20.8%
-20684
 
8.3%
11729
 
4.7%
a10762
 
4.3%
28446
 
3.4%
e7591
 
3.1%
i7104
 
2.9%
n7091
 
2.9%
16945
 
2.8%
/6631
 
2.7%
Other values (154)109563
44.2%

Funding Orgs
Text

Missing 

Distinct2081
Distinct (%)89.6%
Missing2820
Missing (%)54.8%
Memory size463.5 KiB
2026-01-14T11:01:48.954282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length943
Median length356
Mean length116.06202
Min length4

Characters and Unicode

Total characters269496
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1942 ?
Unique (%)83.6%

Sample

1st rowMinistry of Science and Technology of Republic of China [MOST 106-2511-S-468 -005 -MY2]
2nd rowNational Natural Science Foundation of China [61977031]
3rd rowSecond Round of Research Projects for Shanghai Private Colleges [2016-SHNGE-08ZD]
4th rowHasler Foundation; National Science Foundation [0833612, 1345523, 0848962]; Direct For Education and Human Resources; Division Of Research On Learning [0833612] Funding Source: National Science Foundation; Directorate For Engineering [0848962, 1345523] Funding Source: National Science Foundation; Div Of Industrial Innovation & Partnersh [1345523, 0848962] Funding Source: National Science Foundation
5th rowInstitute of Museum and Library Services, USA [LG-14-19-0079-19]
ValueCountFrequency (%)
of2073
 
6.3%
science1422
 
4.3%
national1221
 
3.7%
foundation1200
 
3.6%
and1114
 
3.4%
research899
 
2.7%
education825
 
2.5%
for666
 
2.0%
the661
 
2.0%
university418
 
1.3%
Other values (5172)22606
68.3%
2026-01-14T11:01:49.720493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30786
 
11.4%
n19062
 
7.1%
e16678
 
6.2%
i16290
 
6.0%
a15502
 
5.8%
o15467
 
5.7%
t11341
 
4.2%
r9949
 
3.7%
c9685
 
3.6%
u6688
 
2.5%
Other values (76)118048
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)269496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30786
 
11.4%
n19062
 
7.1%
e16678
 
6.2%
i16290
 
6.0%
a15502
 
5.8%
o15467
 
5.7%
t11341
 
4.2%
r9949
 
3.7%
c9685
 
3.6%
u6688
 
2.5%
Other values (76)118048
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)269496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30786
 
11.4%
n19062
 
7.1%
e16678
 
6.2%
i16290
 
6.0%
a15502
 
5.8%
o15467
 
5.7%
t11341
 
4.2%
r9949
 
3.7%
c9685
 
3.6%
u6688
 
2.5%
Other values (76)118048
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)269496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30786
 
11.4%
n19062
 
7.1%
e16678
 
6.2%
i16290
 
6.0%
a15502
 
5.8%
o15467
 
5.7%
t11341
 
4.2%
r9949
 
3.7%
c9685
 
3.6%
u6688
 
2.5%
Other values (76)118048
43.8%
Distinct1661
Distinct (%)72.2%
Missing2841
Missing (%)55.3%
Memory size496.4 KiB
2026-01-14T11:01:50.168273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length938
Median length418
Mean length131.9409
Min length1

Characters and Unicode

Total characters303596
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1509 ?
Unique (%)65.6%

Sample

1st rowMinistry of Science and Technology of Republic of China(Ministry of Science and Technology, Taiwan)
2nd rowNational Natural Science Foundation of China(National Natural Science Foundation of China (NSFC))
3rd rowSecond Round of Research Projects for Shanghai Private Colleges
4th rowHasler Foundation; National Science Foundation(National Science Foundation (NSF)); Direct For Education and Human Resources; Division Of Research On Learning(National Science Foundation (NSF)NSF - Directorate for STEM Education (EDU)); Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)); Div Of Industrial Innovation & Partnersh(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG))
5th rowInstitute of Museum and Library Services, USA
ValueCountFrequency (%)
of2630
 
6.9%
science2311
 
6.1%
foundation1474
 
3.9%
and1254
 
3.3%
research1117
 
2.9%
1114
 
2.9%
education1074
 
2.8%
for1068
 
2.8%
national941
 
2.5%
the711
 
1.9%
Other values (3562)24359
64.0%
2026-01-14T11:01:50.882268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35755
 
11.8%
n25359
 
8.4%
e22252
 
7.3%
i22112
 
7.3%
o21547
 
7.1%
a21497
 
7.1%
t15642
 
5.2%
c13412
 
4.4%
r12514
 
4.1%
u7963
 
2.6%
Other values (73)105543
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)303596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35755
 
11.8%
n25359
 
8.4%
e22252
 
7.3%
i22112
 
7.3%
o21547
 
7.1%
a21497
 
7.1%
t15642
 
5.2%
c13412
 
4.4%
r12514
 
4.1%
u7963
 
2.6%
Other values (73)105543
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)303596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35755
 
11.8%
n25359
 
8.4%
e22252
 
7.3%
i22112
 
7.3%
o21547
 
7.1%
a21497
 
7.1%
t15642
 
5.2%
c13412
 
4.4%
r12514
 
4.1%
u7963
 
2.6%
Other values (73)105543
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)303596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35755
 
11.8%
n25359
 
8.4%
e22252
 
7.3%
i22112
 
7.3%
o21547
 
7.1%
a21497
 
7.1%
t15642
 
5.2%
c13412
 
4.4%
r12514
 
4.1%
u7963
 
2.6%
Other values (73)105543
34.8%

Funding Text
Text

Missing 

Distinct2166
Distinct (%)93.5%
Missing2826
Missing (%)55.0%
Memory size770.4 KiB
2026-01-14T11:01:51.392575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1613
Median length564
Mean length252.52504
Min length11

Characters and Unicode

Total characters584848
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2100 ?
Unique (%)90.7%

Sample

1st rowThis research was supported by Ministry of Science and Technology of Republic of China under the number of MOST 106-2511-S-468 -005 -MY2.
2nd rowThis work has been finally supported by the National Natural Science Foundation of China (Grant No.61977031) named Research on the Intelligent Comprehensive Assessment of Computational Thinking for the Key Competence.
3rd rowThis Research was financially supported by the Second Round of Research Projects for Shanghai Private Colleges (2016-SHNGE-08ZD, Thanks for the help.
4th rowThis work is supported by the Hasler Foundation and the National Science Foundation under Grant Numbers 0833612, 1345523, and 0848962. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these foundations.
5th rowThis work was supported by the Institute of Museum and Library Services, USA under Grant #LG-14-19-0079-19.
ValueCountFrequency (%)
the6760
 
8.0%
of3565
 
4.2%
and3482
 
4.1%
this2424
 
2.9%
by2255
 
2.7%
research1652
 
1.9%
for1412
 
1.7%
in1356
 
1.6%
supported1320
 
1.6%
science1277
 
1.5%
Other values (9325)59488
70.0%
2026-01-14T11:01:52.750011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82685
 
14.1%
e44355
 
7.6%
n36143
 
6.2%
a35041
 
6.0%
t34360
 
5.9%
o34164
 
5.8%
i32012
 
5.5%
r29464
 
5.0%
s24037
 
4.1%
h18673
 
3.2%
Other values (81)213914
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)584848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
82685
 
14.1%
e44355
 
7.6%
n36143
 
6.2%
a35041
 
6.0%
t34360
 
5.9%
o34164
 
5.8%
i32012
 
5.5%
r29464
 
5.0%
s24037
 
4.1%
h18673
 
3.2%
Other values (81)213914
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)584848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
82685
 
14.1%
e44355
 
7.6%
n36143
 
6.2%
a35041
 
6.0%
t34360
 
5.9%
o34164
 
5.8%
i32012
 
5.5%
r29464
 
5.0%
s24037
 
4.1%
h18673
 
3.2%
Other values (81)213914
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)584848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
82685
 
14.1%
e44355
 
7.6%
n36143
 
6.2%
a35041
 
6.0%
t34360
 
5.9%
o34164
 
5.8%
i32012
 
5.5%
r29464
 
5.0%
s24037
 
4.1%
h18673
 
3.2%
Other values (81)213914
36.6%

Cited References
Unsupported

Missing  Rejected  Unsupported 

Missing5142
Missing (%)100.0%
Memory size40.3 KiB

Cited Reference Count
Real number (ℝ)

High correlation  Zeros 

Distinct182
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.762738
Minimum0
Maximum678
Zeros114
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:53.059027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median35
Q359
95-th percentile100
Maximum678
Range678
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.371033
Coefficient of variation (CV)0.79906239
Kurtosis28.081157
Mean41.762738
Median Absolute Deviation (MAD)20
Skewness2.5458066
Sum214744
Variance1113.6259
MonotonicityNot monotonic
2026-01-14T11:01:53.622457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0114
 
2.2%
1092
 
1.8%
1589
 
1.7%
1685
 
1.7%
2185
 
1.7%
784
 
1.6%
1382
 
1.6%
882
 
1.6%
582
 
1.6%
1981
 
1.6%
Other values (172)4266
83.0%
ValueCountFrequency (%)
0114
2.2%
132
 
0.6%
224
 
0.5%
364
1.2%
457
1.1%
582
1.6%
672
1.4%
784
1.6%
882
1.6%
973
1.4%
ValueCountFrequency (%)
6781
< 0.1%
3101
< 0.1%
2621
< 0.1%
2171
< 0.1%
2082
< 0.1%
2071
< 0.1%
2031
< 0.1%
2011
< 0.1%
1911
< 0.1%
1901
< 0.1%

Times Cited, WoS Core
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct162
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.083042
Minimum0
Maximum3737
Zeros1472
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:54.010448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39
95-th percentile46.95
Maximum3737
Range3737
Interquartile range (IQR)9

Descriptive statistics

Standard deviation65.510836
Coefficient of variation (CV)5.4217173
Kurtosis2088.149
Mean12.083042
Median Absolute Deviation (MAD)3
Skewness39.490117
Sum62131
Variance4291.6696
MonotonicityNot monotonic
2026-01-14T11:01:54.650759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01472
28.6%
1656
12.8%
2434
 
8.4%
3318
 
6.2%
4229
 
4.5%
5211
 
4.1%
6173
 
3.4%
7159
 
3.1%
8111
 
2.2%
9109
 
2.1%
Other values (152)1270
24.7%
ValueCountFrequency (%)
01472
28.6%
1656
12.8%
2434
 
8.4%
3318
 
6.2%
4229
 
4.5%
5211
 
4.1%
6173
 
3.4%
7159
 
3.1%
8111
 
2.2%
9109
 
2.1%
ValueCountFrequency (%)
37371
< 0.1%
13371
< 0.1%
9691
< 0.1%
8161
< 0.1%
7791
< 0.1%
7521
< 0.1%
5451
< 0.1%
4841
< 0.1%
4551
< 0.1%
3841
< 0.1%

Times Cited, All Databases
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct187
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.186698
Minimum0
Maximum4918
Zeros1307
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:55.250214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile56
Maximum4918
Range4918
Interquartile range (IQR)12

Descriptive statistics

Standard deviation85.736543
Coefficient of variation (CV)5.6455027
Kurtosis2136.7135
Mean15.186698
Median Absolute Deviation (MAD)3
Skewness40.13762
Sum78090
Variance7350.7549
MonotonicityNot monotonic
2026-01-14T11:01:55.733858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01307
25.4%
1647
12.6%
2419
 
8.1%
3294
 
5.7%
4236
 
4.6%
5194
 
3.8%
6173
 
3.4%
7163
 
3.2%
8121
 
2.4%
9111
 
2.2%
Other values (177)1477
28.7%
ValueCountFrequency (%)
01307
25.4%
1647
12.6%
2419
 
8.1%
3294
 
5.7%
4236
 
4.6%
5194
 
3.8%
6173
 
3.4%
7163
 
3.2%
8121
 
2.4%
9111
 
2.2%
ValueCountFrequency (%)
49181
< 0.1%
17661
< 0.1%
13141
< 0.1%
10811
< 0.1%
9941
< 0.1%
9181
< 0.1%
7281
< 0.1%
6011
< 0.1%
5451
< 0.1%
4721
< 0.1%

180 Day Usage Count
Real number (ℝ)

High correlation  Zeros 

Distinct87
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0560093
Minimum0
Maximum272
Zeros2249
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:56.127660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum272
Range272
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.882995
Coefficient of variation (CV)2.9297258
Kurtosis167.75952
Mean4.0560093
Median Absolute Deviation (MAD)1
Skewness10.570871
Sum20856
Variance141.20558
MonotonicityNot monotonic
2026-01-14T11:01:56.685399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02249
43.7%
1805
 
15.7%
2426
 
8.3%
3285
 
5.5%
4227
 
4.4%
5193
 
3.8%
6126
 
2.5%
7122
 
2.4%
888
 
1.7%
972
 
1.4%
Other values (77)549
 
10.7%
ValueCountFrequency (%)
02249
43.7%
1805
 
15.7%
2426
 
8.3%
3285
 
5.5%
4227
 
4.4%
5193
 
3.8%
6126
 
2.5%
7122
 
2.4%
888
 
1.7%
972
 
1.4%
ValueCountFrequency (%)
2721
< 0.1%
2591
< 0.1%
2411
< 0.1%
1691
< 0.1%
1681
< 0.1%
1581
< 0.1%
1551
< 0.1%
1451
< 0.1%
1441
< 0.1%
1152
< 0.1%

Since 2013 Usage Count
Real number (ℝ)

High correlation  Zeros 

Distinct283
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.582069
Minimum0
Maximum1076
Zeros430
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:01:56.831108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q336
95-th percentile134
Maximum1076
Range1076
Interquartile range (IQR)33

Descriptive statistics

Standard deviation64.339985
Coefficient of variation (CV)1.9747053
Kurtosis60.15913
Mean32.582069
Median Absolute Deviation (MAD)10
Skewness6.1208886
Sum167537
Variance4139.6337
MonotonicityNot monotonic
2026-01-14T11:01:56.983443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0430
 
8.4%
1339
 
6.6%
3316
 
6.1%
2310
 
6.0%
4251
 
4.9%
5231
 
4.5%
7184
 
3.6%
6148
 
2.9%
8132
 
2.6%
10118
 
2.3%
Other values (273)2683
52.2%
ValueCountFrequency (%)
0430
8.4%
1339
6.6%
2310
6.0%
3316
6.1%
4251
4.9%
5231
4.5%
6148
 
2.9%
7184
3.6%
8132
 
2.6%
9105
 
2.0%
ValueCountFrequency (%)
10761
< 0.1%
9791
< 0.1%
8951
< 0.1%
8531
< 0.1%
8361
< 0.1%
7921
< 0.1%
7291
< 0.1%
6971
< 0.1%
6171
< 0.1%
6131
< 0.1%
Distinct345
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size356.0 KiB
2026-01-14T11:01:57.297017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length75
Median length72
Mean length21.872423
Min length4

Characters and Unicode

Total characters112468
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)3.4%

Sample

1st rowATLANTIS PRESS
2nd rowSPRINGER INTERNATIONAL PUBLISHING AG
3rd rowASSOC COMPUTING MACHINERY
4th rowIEEE
5th rowINFORMATION ENGINEERING RESEARCH INST, USA
ValueCountFrequency (%)
assoc1064
 
6.9%
springer898
 
5.9%
computing880
 
5.7%
machinery880
 
5.7%
ieee729
 
4.8%
700
 
4.6%
ltd687
 
4.5%
publishing371
 
2.4%
ag366
 
2.4%
univ363
 
2.4%
Other values (619)8382
54.7%
2026-01-14T11:01:57.821097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E11213
 
10.0%
I10885
 
9.7%
10178
 
9.0%
N8859
 
7.9%
A7690
 
6.8%
S7515
 
6.7%
R6903
 
6.1%
C6844
 
6.1%
O6048
 
5.4%
T5769
 
5.1%
Other values (46)30564
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)112468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E11213
 
10.0%
I10885
 
9.7%
10178
 
9.0%
N8859
 
7.9%
A7690
 
6.8%
S7515
 
6.7%
R6903
 
6.1%
C6844
 
6.1%
O6048
 
5.4%
T5769
 
5.1%
Other values (46)30564
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)112468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E11213
 
10.0%
I10885
 
9.7%
10178
 
9.0%
N8859
 
7.9%
A7690
 
6.8%
S7515
 
6.7%
R6903
 
6.1%
C6844
 
6.1%
O6048
 
5.4%
T5769
 
5.1%
Other values (46)30564
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)112468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E11213
 
10.0%
I10885
 
9.7%
10178
 
9.0%
N8859
 
7.9%
A7690
 
6.8%
S7515
 
6.7%
R6903
 
6.1%
C6844
 
6.1%
O6048
 
5.4%
T5769
 
5.1%
Other values (46)30564
27.2%
Distinct236
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size285.4 KiB
2026-01-14T11:01:58.134682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length21
Mean length7.8160249
Min length3

Characters and Unicode

Total characters40190
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)2.0%

Sample

1st rowPARIS
2nd rowCHAM
3rd rowNEW YORK
4th rowNEW YORK
5th rowNEWARK
ValueCountFrequency (%)
new1882
24.8%
york1880
24.7%
cham365
 
4.8%
abingdon334
 
4.4%
basel233
 
3.1%
hoboken166
 
2.2%
dordrecht162
 
2.1%
oaks148
 
1.9%
thousand148
 
1.9%
oxford132
 
1.7%
Other values (264)2151
28.3%
2026-01-14T11:01:58.634312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O4015
 
10.0%
N3940
 
9.8%
E3594
 
8.9%
A3233
 
8.0%
R3055
 
7.6%
2459
 
6.1%
K2286
 
5.7%
Y2172
 
5.4%
W2025
 
5.0%
S1444
 
3.6%
Other values (47)11967
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)40190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O4015
 
10.0%
N3940
 
9.8%
E3594
 
8.9%
A3233
 
8.0%
R3055
 
7.6%
2459
 
6.1%
K2286
 
5.7%
Y2172
 
5.4%
W2025
 
5.0%
S1444
 
3.6%
Other values (47)11967
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)40190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O4015
 
10.0%
N3940
 
9.8%
E3594
 
8.9%
A3233
 
8.0%
R3055
 
7.6%
2459
 
6.1%
K2286
 
5.7%
Y2172
 
5.4%
W2025
 
5.0%
S1444
 
3.6%
Other values (47)11967
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)40190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O4015
 
10.0%
N3940
 
9.8%
E3594
 
8.9%
A3233
 
8.0%
R3055
 
7.6%
2459
 
6.1%
K2286
 
5.7%
Y2172
 
5.4%
W2025
 
5.0%
S1444
 
3.6%
Other values (47)11967
29.8%
Distinct367
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size501.0 KiB
2026-01-14T11:01:58.929306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length128
Median length118
Mean length50.741929
Min length26

Characters and Unicode

Total characters260915
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)3.7%

Sample

1st row29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
2nd rowGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
3rd row1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
4th row345 E 47TH ST, NEW YORK, NY 10017 USA
5th row100 CONTINENTAL DR, NEWARK, DE 19713 USA
ValueCountFrequency (%)
new2211
 
5.2%
york2174
 
5.1%
ny1891
 
4.4%
usa1705
 
4.0%
st1039
 
2.4%
broadway859
 
2.0%
united849
 
2.0%
states842
 
2.0%
switzerland721
 
1.7%
e717
 
1.7%
Other values (1648)29632
69.5%
2026-01-14T11:01:59.414548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37498
 
14.4%
A16293
 
6.2%
E15726
 
6.0%
,15544
 
6.0%
N14738
 
5.6%
S11453
 
4.4%
R11101
 
4.3%
T10257
 
3.9%
O9399
 
3.6%
18772
 
3.4%
Other values (60)110134
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)260915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37498
 
14.4%
A16293
 
6.2%
E15726
 
6.0%
,15544
 
6.0%
N14738
 
5.6%
S11453
 
4.4%
R11101
 
4.3%
T10257
 
3.9%
O9399
 
3.6%
18772
 
3.4%
Other values (60)110134
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)260915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37498
 
14.4%
A16293
 
6.2%
E15726
 
6.0%
,15544
 
6.0%
N14738
 
5.6%
S11453
 
4.4%
R11101
 
4.3%
T10257
 
3.9%
O9399
 
3.6%
18772
 
3.4%
Other values (60)110134
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)260915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37498
 
14.4%
A16293
 
6.2%
E15726
 
6.0%
,15544
 
6.0%
N14738
 
5.6%
S11453
 
4.4%
R11101
 
4.3%
T10257
 
3.9%
O9399
 
3.6%
18772
 
3.4%
Other values (60)110134
42.2%

ISSN
Text

Missing 

Distinct659
Distinct (%)19.0%
Missing1667
Missing (%)32.4%
Memory size249.0 KiB
2026-01-14T11:01:59.763376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters31275
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique357 ?
Unique (%)10.3%

Sample

1st row2352-5398
2nd row2474-0217
3rd row1943-6092
4th row0735-6331
5th row2211-1662
ValueCountFrequency (%)
1360-2357186
 
5.4%
0302-9743162
 
4.7%
0190-5848138
 
4.0%
0735-6331107
 
3.1%
0360-131571
 
2.0%
0899-340860
 
1.7%
2165-956760
 
1.7%
1059-014554
 
1.6%
1049-482054
 
1.6%
1648-583152
 
1.5%
Other values (649)2531
72.8%
2026-01-14T11:02:00.238841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14020
12.9%
03811
12.2%
-3475
11.1%
33108
9.9%
22937
9.4%
62582
8.3%
92416
7.7%
52315
7.4%
72296
7.3%
42146
6.9%
Other values (2)2169
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)31275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14020
12.9%
03811
12.2%
-3475
11.1%
33108
9.9%
22937
9.4%
62582
8.3%
92416
7.7%
52315
7.4%
72296
7.3%
42146
6.9%
Other values (2)2169
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14020
12.9%
03811
12.2%
-3475
11.1%
33108
9.9%
22937
9.4%
62582
8.3%
92416
7.7%
52315
7.4%
72296
7.3%
42146
6.9%
Other values (2)2169
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14020
12.9%
03811
12.2%
-3475
11.1%
33108
9.9%
22937
9.4%
62582
8.3%
92416
7.7%
52315
7.4%
72296
7.3%
42146
6.9%
Other values (2)2169
6.9%

eISSN
Text

Missing 

Distinct489
Distinct (%)19.1%
Missing2585
Missing (%)50.3%
Memory size225.7 KiB
2026-01-14T11:02:00.643888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters23013
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique279 ?
Unique (%)10.9%

Sample

1st row1541-4140
2nd row2211-1670
3rd row1573-7608
4th row1573-7608
5th row1099-0542
ValueCountFrequency (%)
1573-7608186
 
7.3%
1611-3349161
 
6.3%
1541-4140107
 
4.2%
2227-710285
 
3.3%
1873-782x71
 
2.8%
1744-517560
 
2.3%
1744-519154
 
2.1%
1573-183954
 
2.1%
2335-897152
 
2.0%
1878-042351
 
2.0%
Other values (479)1676
65.5%
2026-01-14T11:02:01.119429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13695
16.1%
-2557
11.1%
72416
10.5%
32078
9.0%
52077
9.0%
22048
8.9%
41799
7.8%
01646
7.2%
81586
6.9%
91497
6.5%
Other values (2)1614
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)23013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13695
16.1%
-2557
11.1%
72416
10.5%
32078
9.0%
52077
9.0%
22048
8.9%
41799
7.8%
01646
7.2%
81586
6.9%
91497
6.5%
Other values (2)1614
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13695
16.1%
-2557
11.1%
72416
10.5%
32078
9.0%
52077
9.0%
22048
8.9%
41799
7.8%
01646
7.2%
81586
6.9%
91497
6.5%
Other values (2)1614
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13695
16.1%
-2557
11.1%
72416
10.5%
32078
9.0%
52077
9.0%
22048
8.9%
41799
7.8%
01646
7.2%
81586
6.9%
91497
6.5%
Other values (2)1614
7.0%

ISBN
Text

Missing 

Distinct972
Distinct (%)40.4%
Missing2737
Missing (%)53.2%
Memory size250.8 KiB
2026-01-14T11:02:01.397227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length17
Mean length21.31185
Min length13

Characters and Unicode

Total characters51255
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique611 ?
Unique (%)25.4%

Sample

1st row978-94-62520-60-8
2nd row978-3-319-52691-1; 978-3-319-52690-4
3rd row978-1-4503-7210-7
4th row978-1-5386-5059-2
5th row978-1-16275-049-1
ValueCountFrequency (%)
978-1-4503-6793-644
 
1.5%
978-988-77034-4-032
 
1.1%
978-1-4503-5103-430
 
1.0%
978-1-4503-8062-127
 
0.9%
978-3-319-52691-126
 
0.9%
978-3-319-52690-426
 
0.9%
978-1-4503-5890-325
 
0.8%
978-1-4503-2605-622
 
0.7%
979-8-3503-6306-720
 
0.7%
979-8-3503-5150-720
 
0.7%
Other values (1270)2679
90.8%
2026-01-14T11:02:01.797483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-11803
23.0%
95918
11.5%
85092
9.9%
75087
9.9%
34179
 
8.2%
04004
 
7.8%
13960
 
7.7%
43023
 
5.9%
52954
 
5.8%
62298
 
4.5%
Other values (3)2937
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)51255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-11803
23.0%
95918
11.5%
85092
9.9%
75087
9.9%
34179
 
8.2%
04004
 
7.8%
13960
 
7.7%
43023
 
5.9%
52954
 
5.8%
62298
 
4.5%
Other values (3)2937
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)51255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-11803
23.0%
95918
11.5%
85092
9.9%
75087
9.9%
34179
 
8.2%
04004
 
7.8%
13960
 
7.7%
43023
 
5.9%
52954
 
5.8%
62298
 
4.5%
Other values (3)2937
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)51255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-11803
23.0%
95918
11.5%
85092
9.9%
75087
9.9%
34179
 
8.2%
04004
 
7.8%
13960
 
7.7%
43023
 
5.9%
52954
 
5.8%
62298
 
4.5%
Other values (3)2937
 
5.7%

Journal Abbreviation
Text

Missing 

Distinct748
Distinct (%)19.2%
Missing1242
Missing (%)24.2%
Memory size287.3 KiB
2026-01-14T11:02:02.110412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length16.211795
Min length3

Characters and Unicode

Total characters63226
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)10.3%

Sample

1st rowADV SOC SCI EDUC HUM
2nd rowEDUC COMMUN TECHNOL
3rd rowINT CONF SYST INFORM
4th rowS VIS LANG HUM CEN C
5th rowJ EDUC COMPUT RES
ValueCountFrequency (%)
educ1591
 
12.4%
j750
 
5.8%
comput719
 
5.6%
technol509
 
4.0%
sci436
 
3.4%
int401
 
3.1%
conf355
 
2.8%
res343
 
2.7%
proc330
 
2.6%
inf280
 
2.2%
Other values (758)7115
55.5%
2026-01-14T11:02:02.574106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8929
14.1%
E6685
 
10.6%
C6312
 
10.0%
T4323
 
6.8%
N3900
 
6.2%
O3767
 
6.0%
I3171
 
5.0%
U3069
 
4.9%
S3008
 
4.8%
R2628
 
4.2%
Other values (43)17434
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)63226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8929
14.1%
E6685
 
10.6%
C6312
 
10.0%
T4323
 
6.8%
N3900
 
6.2%
O3767
 
6.0%
I3171
 
5.0%
U3069
 
4.9%
S3008
 
4.8%
R2628
 
4.2%
Other values (43)17434
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8929
14.1%
E6685
 
10.6%
C6312
 
10.0%
T4323
 
6.8%
N3900
 
6.2%
O3767
 
6.0%
I3171
 
5.0%
U3069
 
4.9%
S3008
 
4.8%
R2628
 
4.2%
Other values (43)17434
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8929
14.1%
E6685
 
10.6%
C6312
 
10.0%
T4323
 
6.8%
N3900
 
6.2%
O3767
 
6.0%
I3171
 
5.0%
U3069
 
4.9%
S3008
 
4.8%
R2628
 
4.2%
Other values (43)17434
27.6%
Distinct586
Distinct (%)21.7%
Missing2443
Missing (%)47.5%
Memory size255.2 KiB
2026-01-14T11:02:02.954712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length53
Median length37
Mean length18.80993
Min length2

Characters and Unicode

Total characters50768
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)12.2%

Sample

1st rowEduc. Commun. Technol.
2nd rowJ. Educ. Comput. Res.
3rd rowTechnol. Knowl. Learn.
4th rowEduc. Inf. Technol.
5th rowEduc. Inf. Technol.
ValueCountFrequency (%)
educ1401
 
16.8%
j744
 
8.9%
technol555
 
6.7%
comput526
 
6.3%
sci401
 
4.8%
res329
 
4.0%
inf241
 
2.9%
learn230
 
2.8%
int204
 
2.5%
trans97
 
1.2%
Other values (575)3593
43.2%
2026-01-14T11:02:03.486228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.7330
 
14.4%
5622
 
11.1%
c3233
 
6.4%
n2560
 
5.0%
u2431
 
4.8%
e2392
 
4.7%
E2191
 
4.3%
o2125
 
4.2%
t1916
 
3.8%
d1731
 
3.4%
Other values (54)19237
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)50768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.7330
 
14.4%
5622
 
11.1%
c3233
 
6.4%
n2560
 
5.0%
u2431
 
4.8%
e2392
 
4.7%
E2191
 
4.3%
o2125
 
4.2%
t1916
 
3.8%
d1731
 
3.4%
Other values (54)19237
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.7330
 
14.4%
5622
 
11.1%
c3233
 
6.4%
n2560
 
5.0%
u2431
 
4.8%
e2392
 
4.7%
E2191
 
4.3%
o2125
 
4.2%
t1916
 
3.8%
d1731
 
3.4%
Other values (54)19237
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.7330
 
14.4%
5622
 
11.1%
c3233
 
6.4%
n2560
 
5.0%
u2431
 
4.8%
e2392
 
4.7%
E2191
 
4.3%
o2125
 
4.2%
t1916
 
3.8%
d1731
 
3.4%
Other values (54)19237
37.9%

Publication Date
Text

Missing 

Distinct342
Distinct (%)14.4%
Missing2766
Missing (%)53.8%
Memory size209.7 KiB
2026-01-14T11:02:03.741691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length3
Mean length4.0841751
Min length3

Characters and Unicode

Total characters9704
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)8.5%

Sample

1st rowAPR
2nd rowSEP
3rd rowJUL
4th rowAUG
5th rowMAR
ValueCountFrequency (%)
apr226
 
7.2%
dec219
 
6.9%
jun202
 
6.4%
sep202
 
6.4%
jan202
 
6.4%
mar200
 
6.3%
oct195
 
6.2%
jul192
 
6.1%
aug178
 
5.6%
nov173
 
5.5%
Other values (60)1171
37.1%
2026-01-14T11:02:04.143954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A1046
 
10.8%
784
 
8.1%
J657
 
6.8%
N628
 
6.5%
U617
 
6.4%
E603
 
6.2%
2470
 
4.8%
P465
 
4.8%
R462
 
4.8%
C441
 
4.5%
Other values (21)3531
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1046
 
10.8%
784
 
8.1%
J657
 
6.8%
N628
 
6.5%
U617
 
6.4%
E603
 
6.2%
2470
 
4.8%
P465
 
4.8%
R462
 
4.8%
C441
 
4.5%
Other values (21)3531
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1046
 
10.8%
784
 
8.1%
J657
 
6.8%
N628
 
6.5%
U617
 
6.4%
E603
 
6.2%
2470
 
4.8%
P465
 
4.8%
R462
 
4.8%
C441
 
4.5%
Other values (21)3531
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1046
 
10.8%
784
 
8.1%
J657
 
6.8%
N628
 
6.5%
U617
 
6.4%
E603
 
6.2%
2470
 
4.8%
P465
 
4.8%
R462
 
4.8%
C441
 
4.5%
Other values (21)3531
36.4%

Publication Year
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.7802
Minimum1994
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:02:04.250172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile2014
Q12019
median2021
Q32024
95-th percentile2025
Maximum2026
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5434797
Coefficient of variation (CV)0.0017535206
Kurtosis2.4421692
Mean2020.7802
Median Absolute Deviation (MAD)2
Skewness-1.1615206
Sum10390852
Variance12.556248
MonotonicityNot monotonic
2026-01-14T11:02:04.363470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2025677
13.2%
2024632
12.3%
2023583
11.3%
2022567
11.0%
2021565
11.0%
2020479
9.3%
2019426
8.3%
2017315
6.1%
2018314
6.1%
2016148
 
2.9%
Other values (15)436
8.5%
ValueCountFrequency (%)
19941
 
< 0.1%
19951
 
< 0.1%
19971
 
< 0.1%
19991
 
< 0.1%
20062
 
< 0.1%
20076
 
0.1%
20089
 
0.2%
200917
 
0.3%
201032
0.6%
201147
0.9%
ValueCountFrequency (%)
202632
 
0.6%
2025677
13.2%
2024632
12.3%
2023583
11.3%
2022567
11.0%
2021565
11.0%
2020479
9.3%
2019426
8.3%
2018314
6.1%
2017315
6.1%

Volume
Text

Missing 

Distinct418
Distinct (%)14.2%
Missing2192
Missing (%)42.6%
Memory size216.3 KiB
2026-01-14T11:02:04.726533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.2728814
Min length1

Characters and Unicode

Total characters6705
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)7.4%

Sample

1st row18
2nd row60
3rd row28
4th row27
5th row28
ValueCountFrequency (%)
29106
 
3.6%
14104
 
3.5%
1392
 
3.1%
2883
 
2.8%
1279
 
2.7%
1573
 
2.5%
3070
 
2.4%
1170
 
2.4%
3264
 
2.2%
1060
 
2.0%
Other values (408)2149
72.8%
2026-01-14T11:02:05.205536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11479
22.1%
21100
16.4%
3800
11.9%
5590
 
8.8%
4552
 
8.2%
6499
 
7.4%
9464
 
6.9%
0450
 
6.7%
8393
 
5.9%
7373
 
5.6%
Other values (2)5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6705
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11479
22.1%
21100
16.4%
3800
11.9%
5590
 
8.8%
4552
 
8.2%
6499
 
7.4%
9464
 
6.9%
0450
 
6.7%
8393
 
5.9%
7373
 
5.6%
Other values (2)5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6705
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11479
22.1%
21100
16.4%
3800
11.9%
5590
 
8.8%
4552
 
8.2%
6499
 
7.4%
9464
 
6.9%
0450
 
6.7%
8393
 
5.9%
7373
 
5.6%
Other values (2)5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6705
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11479
22.1%
21100
16.4%
3800
11.9%
5590
 
8.8%
4552
 
8.2%
6499
 
7.4%
9464
 
6.9%
0450
 
6.7%
8393
 
5.9%
7373
 
5.6%
Other values (2)5
 
0.1%

Issue
Text

Missing 

Distinct92
Distinct (%)4.4%
Missing3041
Missing (%)59.1%
Memory size198.1 KiB
2026-01-14T11:02:05.451283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.1761066
Min length1

Characters and Unicode

Total characters2471
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)2.1%

Sample

1st row2
2nd row3
3rd row6
4th row8
5th row2
ValueCountFrequency (%)
1429
20.4%
2341
16.2%
3304
14.5%
4258
12.3%
6145
 
6.9%
5129
 
6.1%
776
 
3.6%
852
 
2.5%
950
 
2.4%
1038
 
1.8%
Other values (82)281
13.4%
2026-01-14T11:02:05.814444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1656
26.5%
2430
17.4%
3363
14.7%
4301
12.2%
6188
 
7.6%
5157
 
6.4%
7109
 
4.4%
892
 
3.7%
071
 
2.9%
966
 
2.7%
Other values (12)38
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1656
26.5%
2430
17.4%
3363
14.7%
4301
12.2%
6188
 
7.6%
5157
 
6.4%
7109
 
4.4%
892
 
3.7%
071
 
2.9%
966
 
2.7%
Other values (12)38
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1656
26.5%
2430
17.4%
3363
14.7%
4301
12.2%
6188
 
7.6%
5157
 
6.4%
7109
 
4.4%
892
 
3.7%
071
 
2.9%
966
 
2.7%
Other values (12)38
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1656
26.5%
2430
17.4%
3363
14.7%
4301
12.2%
6188
 
7.6%
5157
 
6.4%
7109
 
4.4%
892
 
3.7%
071
 
2.9%
966
 
2.7%
Other values (12)38
 
1.5%

Part Number
Text

Missing 

Distinct6
Distinct (%)66.7%
Missing5133
Missing (%)99.8%
Memory size161.0 KiB
2026-01-14T11:02:05.930073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)33.3%

Sample

1st rowA
2nd row2
3rd rowE
4th row1
5th rowB
ValueCountFrequency (%)
22
22.2%
12
22.2%
b2
22.2%
a1
11.1%
e1
11.1%
c1
11.1%
2026-01-14T11:02:06.134633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
22.2%
12
22.2%
B2
22.2%
A1
11.1%
E1
11.1%
C1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22
22.2%
12
22.2%
B2
22.2%
A1
11.1%
E1
11.1%
C1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22
22.2%
12
22.2%
B2
22.2%
A1
11.1%
E1
11.1%
C1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22
22.2%
12
22.2%
B2
22.2%
A1
11.1%
E1
11.1%
C1
11.1%

Supplement
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)23.5%
Missing5125
Missing (%)99.7%
Memory size281.2 KiB
1
14 
3
 
1
S1
 
1
S
 
1

Length

Max length2
Median length1
Mean length1.0588235
Min length1

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)17.6%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
114
 
0.3%
31
 
< 0.1%
S11
 
< 0.1%
S1
 
< 0.1%
(Missing)5125
99.7%

Length

2026-01-14T11:02:06.238572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:02:06.319822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
114
82.4%
31
 
5.9%
s11
 
5.9%
s1
 
5.9%

Most occurring characters

ValueCountFrequency (%)
115
83.3%
S2
 
11.1%
31
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
115
83.3%
S2
 
11.1%
31
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
115
83.3%
S2
 
11.1%
31
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
115
83.3%
S2
 
11.1%
31
 
5.6%

Special Issue
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)1.0%
Missing4937
Missing (%)96.0%
Memory size280.3 KiB
SI
203 
2
 
2

Length

Max length2
Median length2
Mean length1.9902439
Min length1

Characters and Unicode

Total characters408
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI203
 
3.9%
22
 
< 0.1%
(Missing)4937
96.0%

Length

2026-01-14T11:02:06.422604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:02:06.490225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si203
99.0%
22
 
1.0%

Most occurring characters

ValueCountFrequency (%)
S203
49.8%
I203
49.8%
22
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S203
49.8%
I203
49.8%
22
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S203
49.8%
I203
49.8%
22
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S203
49.8%
I203
49.8%
22
 
0.5%

Meeting Abstract
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing5138
Missing (%)99.9%
Memory size160.9 KiB
2026-01-14T11:02:06.614752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length6.5
Mean length5
Min length3

Characters and Unicode

Total characters20
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row145
2nd row2078
3rd rowLA18
4th rowLHMM23011
ValueCountFrequency (%)
1451
25.0%
20781
25.0%
la181
25.0%
lhmm230111
25.0%
2026-01-14T11:02:07.005493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
20.0%
22
10.0%
82
10.0%
02
10.0%
L2
10.0%
M2
10.0%
51
 
5.0%
41
 
5.0%
71
 
5.0%
A1
 
5.0%
Other values (2)2
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14
20.0%
22
10.0%
82
10.0%
02
10.0%
L2
10.0%
M2
10.0%
51
 
5.0%
41
 
5.0%
71
 
5.0%
A1
 
5.0%
Other values (2)2
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14
20.0%
22
10.0%
82
10.0%
02
10.0%
L2
10.0%
M2
10.0%
51
 
5.0%
41
 
5.0%
71
 
5.0%
A1
 
5.0%
Other values (2)2
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14
20.0%
22
10.0%
82
10.0%
02
10.0%
L2
10.0%
M2
10.0%
51
 
5.0%
41
 
5.0%
71
 
5.0%
A1
 
5.0%
Other values (2)2
10.0%

Start Page
Text

Missing 

Distinct1568
Distinct (%)42.4%
Missing1440
Missing (%)28.0%
Memory size233.2 KiB
2026-01-14T11:02:07.620672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0229606
Min length1

Characters and Unicode

Total characters11191
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique893 ?
Unique (%)24.1%

Sample

1st row817
2nd row291
3rd row364
4th row129
5th row376
ValueCountFrequency (%)
147
 
1.3%
325
 
0.7%
5515
 
0.4%
21314
 
0.4%
5714
 
0.4%
8514
 
0.4%
13313
 
0.4%
6512
 
0.3%
3312
 
0.3%
1512
 
0.3%
Other values (1558)3524
95.2%
2026-01-14T11:02:08.466447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11941
17.3%
31366
12.2%
21263
11.3%
51183
10.6%
41018
9.1%
71004
9.0%
9951
8.5%
6916
8.2%
8796
7.1%
0721
 
6.4%
Other values (6)32
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11941
17.3%
31366
12.2%
21263
11.3%
51183
10.6%
41018
9.1%
71004
9.0%
9951
8.5%
6916
8.2%
8796
7.1%
0721
 
6.4%
Other values (6)32
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11941
17.3%
31366
12.2%
21263
11.3%
51183
10.6%
41018
9.1%
71004
9.0%
9951
8.5%
6916
8.2%
8796
7.1%
0721
 
6.4%
Other values (6)32
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11941
17.3%
31366
12.2%
21263
11.3%
51183
10.6%
41018
9.1%
71004
9.0%
9951
8.5%
6916
8.2%
8796
7.1%
0721
 
6.4%
Other values (6)32
 
0.3%

End Page
Text

Missing 

Distinct1586
Distinct (%)42.8%
Missing1440
Missing (%)28.0%
Memory size233.3 KiB
2026-01-14T11:02:09.114865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0599676
Min length1

Characters and Unicode

Total characters11328
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique886 ?
Unique (%)23.9%

Sample

1st row821
2nd row305
3rd row368
4th row131
5th row380
ValueCountFrequency (%)
16
 
0.4%
2813
 
0.4%
8012
 
0.3%
5712
 
0.3%
13812
 
0.3%
1712
 
0.3%
11212
 
0.3%
6212
 
0.3%
4111
 
0.3%
2411
 
0.3%
Other values (1576)3579
96.7%
2026-01-14T11:02:09.917721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11874
16.5%
21434
12.7%
31203
10.6%
41130
10.0%
51046
9.2%
61015
9.0%
8958
8.5%
7906
8.0%
0875
7.7%
9850
7.5%
Other values (7)37
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11874
16.5%
21434
12.7%
31203
10.6%
41130
10.0%
51046
9.2%
61015
9.0%
8958
8.5%
7906
8.0%
0875
7.7%
9850
7.5%
Other values (7)37
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11874
16.5%
21434
12.7%
31203
10.6%
41130
10.0%
51046
9.2%
61015
9.0%
8958
8.5%
7906
8.0%
0875
7.7%
9850
7.5%
Other values (7)37
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11874
16.5%
21434
12.7%
31203
10.6%
41130
10.0%
51046
9.2%
61015
9.0%
8958
8.5%
7906
8.0%
0875
7.7%
9850
7.5%
Other values (7)37
 
0.3%

Article Number
Text

Missing 

Distinct796
Distinct (%)82.3%
Missing4175
Missing (%)81.2%
Memory size181.7 KiB
2026-01-14T11:02:10.367230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length5.131334
Min length1

Characters and Unicode

Total characters4962
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique732 ?
Unique (%)75.7%

Sample

1st row7356331211033158
2nd row15
3rd row735633120965919
4th row20133
5th row735633120988807
ValueCountFrequency (%)
1910
 
1.0%
19
 
0.9%
58
 
0.8%
68
 
0.8%
128
 
0.8%
118
 
0.8%
107
 
0.7%
77
 
0.7%
276
 
0.6%
46
 
0.6%
Other values (786)890
92.0%
2026-01-14T11:02:10.976702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1961
19.4%
0727
14.7%
2523
10.5%
3486
9.8%
5413
8.3%
4400
8.1%
7372
 
7.5%
6335
 
6.8%
9324
 
6.5%
8307
 
6.2%
Other values (20)114
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1961
19.4%
0727
14.7%
2523
10.5%
3486
9.8%
5413
8.3%
4400
8.1%
7372
 
7.5%
6335
 
6.8%
9324
 
6.5%
8307
 
6.2%
Other values (20)114
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1961
19.4%
0727
14.7%
2523
10.5%
3486
9.8%
5413
8.3%
4400
8.1%
7372
 
7.5%
6335
 
6.8%
9324
 
6.5%
8307
 
6.2%
Other values (20)114
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1961
19.4%
0727
14.7%
2523
10.5%
3486
9.8%
5413
8.3%
4400
8.1%
7372
 
7.5%
6335
 
6.8%
9324
 
6.5%
8307
 
6.2%
Other values (20)114
 
2.3%

DOI
Text

Missing 

Distinct4191
Distinct (%)> 99.9%
Missing950
Missing (%)18.5%
Memory size333.0 KiB
2026-01-14T11:02:11.287118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length43
Mean length25.056536
Min length13

Characters and Unicode

Total characters105037
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4190 ?
Unique (%)> 99.9%

Sample

1st row10.1007/978-3-319-52691-1_18
2nd row10.1145/3345120.3345134
3rd row10.1109/IC3.2018.00-45
4th row10.1145/3629296.3629361
5th row10.1145/3649217.3653565
ValueCountFrequency (%)
10.1145/3408877.34396392
 
< 0.1%
10.1080/03057267.2021.19635801
 
< 0.1%
10.1145/3514262.35143111
 
< 0.1%
10.1109/weit.2013.271
 
< 0.1%
10.1007/978-3-031-38454-7_121
 
< 0.1%
10.1109/icetc.2009.161
 
< 0.1%
10.4028/www.scientific.net/amm.373-375.22001
 
< 0.1%
10.1002/cae.226691
 
< 0.1%
10.1145/3345120.33451341
 
< 0.1%
10.1007/s10639-022-11454-11
 
< 0.1%
Other values (4181)4181
99.7%
2026-01-14T11:02:11.746797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (58)22448
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (58)22448
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (58)22448
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (58)22448
21.4%

DOI Link
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing950
Missing (%)18.5%
Memory size265.0 KiB
0.0
4192 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12576
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04192
81.5%
(Missing)950
 
18.5%

Length

2026-01-14T11:02:11.873129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:02:11.939271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04192
100.0%

Most occurring characters

ValueCountFrequency (%)
08384
66.7%
.4192
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08384
66.7%
.4192
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08384
66.7%
.4192
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08384
66.7%
.4192
33.3%

Book DOI
Categorical

High correlation  Missing 

Distinct31
Distinct (%)42.5%
Missing5069
Missing (%)98.6%
Memory size282.6 KiB
10.1007/978-3-319-52691-1
25 
10.1007/978-3-319-71054-9
10.4102/aosis.2023.BK409
10.7551/mitpress/11209.001.0001
 
3
10.1017/ 9781108654555
 
3
Other values (26)
32 

Length

Max length31
Median length25
Mean length24.780822
Min length21

Characters and Unicode

Total characters1809
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)28.8%

Sample

1st row10.1007/978-3-319-52691-1
2nd row10.1007/978-3-319-52691-1
3rd row10.1007/978-3-319-52691-1
4th row10.1007/978-3-319-52691-1
5th row10.1007/978-3-319-52691-1

Common Values

ValueCountFrequency (%)
10.1007/978-3-319-52691-125
 
0.5%
10.1007/978-3-319-71054-96
 
0.1%
10.4102/aosis.2023.BK4094
 
0.1%
10.7551/mitpress/11209.001.00013
 
0.1%
10.1017/ 97811086545553
 
0.1%
10.1007/978-3-319-66020-23
 
0.1%
10.4018/978-1-4666-1864-02
 
< 0.1%
10.4018/978-1-5225-2255-32
 
< 0.1%
10.4018/978-1-4666-0182-62
 
< 0.1%
10.4324/97804293195012
 
< 0.1%
Other values (21)21
 
0.4%
(Missing)5069
98.6%

Length

2026-01-14T11:02:12.016845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.1007/978-3-319-52691-125
32.9%
10.1007/978-3-319-71054-96
 
7.9%
10.4102/aosis.2023.bk4094
 
5.3%
10.7551/mitpress/11209.001.00013
 
3.9%
10.10173
 
3.9%
97811086545553
 
3.9%
10.1007/978-3-319-66020-23
 
3.9%
10.4018/978-1-4666-1864-02
 
2.6%
10.4018/978-1-5225-2255-32
 
2.6%
10.4018/978-1-4666-0182-62
 
2.6%
Other values (22)23
30.3%

Most occurring characters

ValueCountFrequency (%)
1310
17.1%
0261
14.4%
-220
12.2%
9162
9.0%
7134
7.4%
3107
 
5.9%
897
 
5.4%
.89
 
4.9%
678
 
4.3%
/77
 
4.3%
Other values (15)274
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1310
17.1%
0261
14.4%
-220
12.2%
9162
9.0%
7134
7.4%
3107
 
5.9%
897
 
5.4%
.89
 
4.9%
678
 
4.3%
/77
 
4.3%
Other values (15)274
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1310
17.1%
0261
14.4%
-220
12.2%
9162
9.0%
7134
7.4%
3107
 
5.9%
897
 
5.4%
.89
 
4.9%
678
 
4.3%
/77
 
4.3%
Other values (15)274
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1310
17.1%
0261
14.4%
-220
12.2%
9162
9.0%
7134
7.4%
3107
 
5.9%
897
 
5.4%
.89
 
4.9%
678
 
4.3%
/77
 
4.3%
Other values (15)274
15.1%

Early Access Date
Date

Missing 

Distinct84
Distinct (%)6.9%
Missing3926
Missing (%)76.4%
Memory size40.3 KiB
Minimum2019-03-01 00:00:00
Maximum2026-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T11:02:12.139841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:02:12.317350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Number of Pages
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.878063
Minimum1
Maximum433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:02:12.471752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q319
95-th percentile31
Maximum433
Range432
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.908785
Coefficient of variation (CV)1.0022137
Kurtosis361.85436
Mean13.878063
Median Absolute Deviation (MAD)6
Skewness14.002221
Sum71361
Variance193.4543
MonotonicityNot monotonic
2026-01-14T11:02:12.612403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6384
 
7.5%
7330
 
6.4%
5273
 
5.3%
8258
 
5.0%
4235
 
4.6%
12232
 
4.5%
10222
 
4.3%
9217
 
4.2%
16196
 
3.8%
2189
 
3.7%
Other values (56)2606
50.7%
ValueCountFrequency (%)
1156
3.0%
2189
3.7%
3110
 
2.1%
4235
4.6%
5273
5.3%
6384
7.5%
7330
6.4%
8258
5.0%
9217
4.2%
10222
4.3%
ValueCountFrequency (%)
4331
< 0.1%
4231
< 0.1%
2781
< 0.1%
2661
< 0.1%
1921
< 0.1%
1851
< 0.1%
1441
< 0.1%
801
< 0.1%
761
< 0.1%
661
< 0.1%
Distinct473
Distinct (%)9.2%
Missing15
Missing (%)0.3%
Memory size560.0 KiB
2026-01-14T11:02:12.809490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length258
Median length187
Mean length62.722255
Min length3

Characters and Unicode

Total characters321577
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243 ?
Unique (%)4.7%

Sample

1st rowEducation & Educational Research; Social Sciences, Interdisciplinary
2nd rowEducation & Educational Research; Social Issues
3rd rowComputer Science, Interdisciplinary Applications; Computer Science, Theory & Methods
4th rowComputer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic
5th rowEconomics; Education & Educational Research; Management
ValueCountFrequency (%)
education4149
12.4%
4144
12.4%
science3348
10.0%
computer3174
 
9.5%
educational2673
 
8.0%
research2632
 
7.8%
scientific1512
 
4.5%
disciplines1512
 
4.5%
interdisciplinary1179
 
3.5%
applications1086
 
3.2%
Other values (157)8134
24.2%
2026-01-14T11:02:13.172462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i33260
 
10.3%
28416
 
8.8%
e27741
 
8.6%
c27355
 
8.5%
n23457
 
7.3%
t18983
 
5.9%
a17569
 
5.5%
o16158
 
5.0%
r13305
 
4.1%
s11632
 
3.6%
Other values (38)103701
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)321577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i33260
 
10.3%
28416
 
8.8%
e27741
 
8.6%
c27355
 
8.5%
n23457
 
7.3%
t18983
 
5.9%
a17569
 
5.5%
o16158
 
5.0%
r13305
 
4.1%
s11632
 
3.6%
Other values (38)103701
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)321577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i33260
 
10.3%
28416
 
8.8%
e27741
 
8.6%
c27355
 
8.5%
n23457
 
7.3%
t18983
 
5.9%
a17569
 
5.5%
o16158
 
5.0%
r13305
 
4.1%
s11632
 
3.6%
Other values (38)103701
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)321577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i33260
 
10.3%
28416
 
8.8%
e27741
 
8.6%
c27355
 
8.5%
n23457
 
7.3%
t18983
 
5.9%
a17569
 
5.5%
o16158
 
5.0%
r13305
 
4.1%
s11632
 
3.6%
Other values (38)103701
32.2%

Web of Science Index
Categorical

High correlation 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size577.6 KiB
Conference Proceedings Citation Index - Science (CPCI-S)
1439 
Social Science Citation Index (SSCI)
991 
Emerging Sources Citation Index (ESCI)
961 
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
703 
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
355 
Other values (14)
693 

Length

Max length185
Median length136
Mean length62.674057
Min length36

Characters and Unicode

Total characters322270
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
2nd rowBook Citation Index– Social Sciences & Humanities (BKCI-SSH)
3rd rowConference Proceedings Citation Index - Science (CPCI-S)
4th rowConference Proceedings Citation Index - Science (CPCI-S)
5th rowConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)

Common Values

ValueCountFrequency (%)
Conference Proceedings Citation Index - Science (CPCI-S)1439
28.0%
Social Science Citation Index (SSCI)991
19.3%
Emerging Sources Citation Index (ESCI)961
18.7%
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)703
13.7%
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)355
 
6.9%
Science Citation Index Expanded (SCI-EXPANDED)289
 
5.6%
Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)231
 
4.5%
Book Citation Index– Social Sciences & Humanities (BKCI-SSH)56
 
1.1%
Book Citation Index– Social Sciences & Humanities (BKCI-SSH); Book Citation Index– Science (BKCI-S)35
 
0.7%
Arts & Humanities Citation Index (A&HCI)25
 
0.5%
Other values (9)57
 
1.1%

Length

2026-01-14T11:02:13.307657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
citation6292
15.4%
index6152
15.1%
science5182
12.7%
4174
10.2%
conference3089
7.6%
proceedings3089
7.6%
social2405
 
5.9%
cpci-s2153
 
5.3%
ssci1377
 
3.4%
humanities1085
 
2.7%
Other values (13)5781
14.2%

Most occurring characters

ValueCountFrequency (%)
e35960
 
11.2%
35637
 
11.1%
n29839
 
9.3%
i26483
 
8.2%
c20092
 
6.2%
C18762
 
5.8%
S17280
 
5.4%
o16116
 
5.0%
t13726
 
4.3%
I12584
 
3.9%
Other values (27)95791
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)322270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e35960
 
11.2%
35637
 
11.1%
n29839
 
9.3%
i26483
 
8.2%
c20092
 
6.2%
C18762
 
5.8%
S17280
 
5.4%
o16116
 
5.0%
t13726
 
4.3%
I12584
 
3.9%
Other values (27)95791
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)322270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e35960
 
11.2%
35637
 
11.1%
n29839
 
9.3%
i26483
 
8.2%
c20092
 
6.2%
C18762
 
5.8%
S17280
 
5.4%
o16116
 
5.0%
t13726
 
4.3%
I12584
 
3.9%
Other values (27)95791
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)322270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e35960
 
11.2%
35637
 
11.1%
n29839
 
9.3%
i26483
 
8.2%
c20092
 
6.2%
C18762
 
5.8%
S17280
 
5.4%
o16116
 
5.0%
t13726
 
4.3%
I12584
 
3.9%
Other values (27)95791
29.7%
Distinct227
Distinct (%)4.4%
Missing15
Missing (%)0.3%
Memory size427.1 KiB
2026-01-14T11:02:13.604159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length140
Median length139
Mean length36.180417
Min length3

Characters and Unicode

Total characters185497
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)2.6%

Sample

1st rowEducation & Educational Research; Social Sciences - Other Topics
2nd rowEducation & Educational Research; Social Issues
3rd rowComputer Science
4th rowComputer Science; Engineering
5th rowBusiness & Economics; Education & Educational Research
ValueCountFrequency (%)
4196
18.7%
research3606
16.0%
education3599
16.0%
educational3599
16.0%
science2342
10.4%
computer2131
9.5%
engineering666
 
3.0%
other219
 
1.0%
topics219
 
1.0%
psychology195
 
0.9%
Other values (120)1721
7.7%
2026-01-14T11:02:14.088012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17366
 
9.4%
c17252
 
9.3%
e16869
 
9.1%
a15249
 
8.2%
n12667
 
6.8%
i12594
 
6.8%
o11526
 
6.2%
t10503
 
5.7%
u9780
 
5.3%
E8025
 
4.3%
Other values (37)53666
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)185497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17366
 
9.4%
c17252
 
9.3%
e16869
 
9.1%
a15249
 
8.2%
n12667
 
6.8%
i12594
 
6.8%
o11526
 
6.2%
t10503
 
5.7%
u9780
 
5.3%
E8025
 
4.3%
Other values (37)53666
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)185497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17366
 
9.4%
c17252
 
9.3%
e16869
 
9.1%
a15249
 
8.2%
n12667
 
6.8%
i12594
 
6.8%
o11526
 
6.2%
t10503
 
5.7%
u9780
 
5.3%
E8025
 
4.3%
Other values (37)53666
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)185497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17366
 
9.4%
c17252
 
9.3%
e16869
 
9.1%
a15249
 
8.2%
n12667
 
6.8%
i12594
 
6.8%
o11526
 
6.2%
t10503
 
5.7%
u9780
 
5.3%
E8025
 
4.3%
Other values (37)53666
28.9%
Distinct3146
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size271.3 KiB
2026-01-14T11:02:14.492834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters25710
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2482 ?
Unique (%)48.3%

Sample

1st rowBD3LM
2nd rowBJ2PE
3rd rowBO5OP
4th rowBM3GW
5th rowBGC87
ValueCountFrequency (%)
bt2kw44
 
0.9%
bp1xa32
 
0.6%
bn4hw30
 
0.6%
by3po27
 
0.5%
bj2pe26
 
0.5%
bq1ej25
 
0.5%
bo0zi22
 
0.4%
by4or20
 
0.4%
bt3ph19
 
0.4%
bm7tq17
 
0.3%
Other values (3136)4880
94.9%
2026-01-14T11:02:14.989406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B2926
 
11.4%
P809
 
3.1%
1807
 
3.1%
4778
 
3.0%
W759
 
3.0%
3750
 
2.9%
2749
 
2.9%
6746
 
2.9%
0723
 
2.8%
O721
 
2.8%
Other values (26)15942
62.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)25710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B2926
 
11.4%
P809
 
3.1%
1807
 
3.1%
4778
 
3.0%
W759
 
3.0%
3750
 
2.9%
2749
 
2.9%
6746
 
2.9%
0723
 
2.8%
O721
 
2.8%
Other values (26)15942
62.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B2926
 
11.4%
P809
 
3.1%
1807
 
3.1%
4778
 
3.0%
W759
 
3.0%
3750
 
2.9%
2749
 
2.9%
6746
 
2.9%
0723
 
2.8%
O721
 
2.8%
Other values (26)15942
62.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B2926
 
11.4%
P809
 
3.1%
1807
 
3.1%
4778
 
3.0%
W759
 
3.0%
3750
 
2.9%
2749
 
2.9%
6746
 
2.9%
0723
 
2.8%
O721
 
2.8%
Other values (26)15942
62.0%

Pubmed Id
Real number (ℝ)

High correlation  Missing 

Distinct163
Distinct (%)100.0%
Missing4979
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean35298568
Minimum7724567
Maximum41315544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.3 KiB
2026-01-14T11:02:15.130483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7724567
5-th percentile25411797
Q134480024
median35769741
Q337663861
95-th percentile40809543
Maximum41315544
Range33590977
Interquartile range (IQR)3183836.5

Descriptive statistics

Standard deviation4664770.2
Coefficient of variation (CV)0.13215183
Kurtosis8.5704868
Mean35298568
Median Absolute Deviation (MAD)1592030
Skewness-2.3141949
Sum5.7536665 × 109
Variance2.1760081 × 1013
MonotonicityNot monotonic
2026-01-14T11:02:15.280821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410941171
 
< 0.1%
364674411
 
< 0.1%
348249971
 
< 0.1%
389216901
 
< 0.1%
345667351
 
< 0.1%
390354811
 
< 0.1%
356321571
 
< 0.1%
365823271
 
< 0.1%
356275391
 
< 0.1%
186724621
 
< 0.1%
Other values (153)153
 
3.0%
(Missing)4979
96.8%
ValueCountFrequency (%)
77245671
< 0.1%
186724621
< 0.1%
215639751
< 0.1%
220683291
< 0.1%
231943711
< 0.1%
233852871
< 0.1%
234490031
< 0.1%
247195751
< 0.1%
254117921
< 0.1%
254118391
< 0.1%
ValueCountFrequency (%)
413155441
< 0.1%
413052651
< 0.1%
412954261
< 0.1%
410941171
< 0.1%
410065371
< 0.1%
410005241
< 0.1%
409018151
< 0.1%
408971461
< 0.1%
408205121
< 0.1%
407108211
< 0.1%

Open Access Designations
Categorical

High correlation  Missing 

Distinct28
Distinct (%)1.7%
Missing3457
Missing (%)67.2%
Memory size288.6 KiB
gold
665 
Green Submitted, gold
409 
hybrid
240 
Green Submitted
105 
Green Submitted, hybrid
80 
Other values (23)
186 

Length

Max length40
Median length39
Mean length11.400593
Min length4

Characters and Unicode

Total characters19210
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.5%

Sample

1st rowGreen Submitted, hybrid
2nd rowhybrid
3rd rowhybrid
4th rowhybrid
5th rowGreen Accepted

Common Values

ValueCountFrequency (%)
gold665
 
12.9%
Green Submitted, gold409
 
8.0%
hybrid240
 
4.7%
Green Submitted105
 
2.0%
Green Submitted, hybrid80
 
1.6%
Bronze72
 
1.4%
Green Submitted, Bronze36
 
0.7%
Green Published16
 
0.3%
Green Published, hybrid8
 
0.2%
Green Published, gold7
 
0.1%
Other values (18)47
 
0.9%
(Missing)3457
67.2%

Length

2026-01-14T11:02:15.461215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gold1098
36.5%
green728
24.2%
submitted649
21.6%
hybrid336
 
11.2%
bronze120
 
4.0%
published47
 
1.6%
accepted32
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e2336
12.2%
d2162
11.3%
t1330
 
6.9%
1325
 
6.9%
o1218
 
6.3%
r1184
 
6.2%
l1145
 
6.0%
g1098
 
5.7%
b1032
 
5.4%
i1032
 
5.4%
Other values (15)5348
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)19210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2336
12.2%
d2162
11.3%
t1330
 
6.9%
1325
 
6.9%
o1218
 
6.3%
r1184
 
6.2%
l1145
 
6.0%
g1098
 
5.7%
b1032
 
5.4%
i1032
 
5.4%
Other values (15)5348
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2336
12.2%
d2162
11.3%
t1330
 
6.9%
1325
 
6.9%
o1218
 
6.3%
r1184
 
6.2%
l1145
 
6.0%
g1098
 
5.7%
b1032
 
5.4%
i1032
 
5.4%
Other values (15)5348
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2336
12.2%
d2162
11.3%
t1330
 
6.9%
1325
 
6.9%
o1218
 
6.3%
r1184
 
6.2%
l1145
 
6.0%
g1098
 
5.7%
b1032
 
5.4%
i1032
 
5.4%
Other values (15)5348
27.8%

Highly Cited Status
Boolean

Constant  Missing 

Distinct1
Distinct (%)7.1%
Missing5128
Missing (%)99.7%
Memory size10.2 KiB
True
 
14
(Missing)
5128 
ValueCountFrequency (%)
True14
 
0.3%
(Missing)5128
99.7%
2026-01-14T11:02:15.536836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Hot Paper Status
Boolean

Constant  Missing 

Distinct1
Distinct (%)7.1%
Missing5128
Missing (%)99.7%
Memory size10.2 KiB
False
 
14
(Missing)
5128 
ValueCountFrequency (%)
False14
 
0.3%
(Missing)5128
99.7%
2026-01-14T11:02:15.572370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Date of Export
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.4 KiB
2025-12-30
5142 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters51420
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-12-30
2nd row2025-12-30
3rd row2025-12-30
4th row2025-12-30
5th row2025-12-30

Common Values

ValueCountFrequency (%)
2025-12-305142
100.0%

Length

2026-01-14T11:02:16.646365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:02:16.717502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025-12-305142
100.0%

Most occurring characters

ValueCountFrequency (%)
215426
30.0%
010284
20.0%
-10284
20.0%
55142
 
10.0%
15142
 
10.0%
35142
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)51420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
215426
30.0%
010284
20.0%
-10284
20.0%
55142
 
10.0%
15142
 
10.0%
35142
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)51420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
215426
30.0%
010284
20.0%
-10284
20.0%
55142
 
10.0%
15142
 
10.0%
35142
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)51420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
215426
30.0%
010284
20.0%
-10284
20.0%
55142
 
10.0%
15142
 
10.0%
35142
 
10.0%

UT (Unique WOS ID)
Text

Unique 

Distinct5142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size341.6 KiB
2026-01-14T11:02:16.922555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters97698
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5142 ?
Unique (%)100.0%

Sample

1st rowWOS:000359863700211
2nd rowWOS:000419747600019
3rd rowWOS:000518574900075
4th rowWOS:000462080100026
5th rowWOS:000322345500073
ValueCountFrequency (%)
wos:0012658726000931
 
< 0.1%
wos:0013886942000011
 
< 0.1%
wos:0003598637002111
 
< 0.1%
wos:0004197476000191
 
< 0.1%
wos:0005185749000751
 
< 0.1%
wos:0004620801000261
 
< 0.1%
wos:0011981384000011
 
< 0.1%
wos:0013036124001811
 
< 0.1%
wos:0005580888060301
 
< 0.1%
wos:0004474088020791
 
< 0.1%
Other values (5132)5132
99.8%
2026-01-14T11:02:17.297797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
034601
35.4%
18408
 
8.6%
W5142
 
5.3%
S5142
 
5.3%
O5142
 
5.3%
:5142
 
5.3%
45024
 
5.1%
24593
 
4.7%
54504
 
4.6%
34471
 
4.6%
Other values (9)15529
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)97698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034601
35.4%
18408
 
8.6%
W5142
 
5.3%
S5142
 
5.3%
O5142
 
5.3%
:5142
 
5.3%
45024
 
5.1%
24593
 
4.7%
54504
 
4.6%
34471
 
4.6%
Other values (9)15529
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)97698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034601
35.4%
18408
 
8.6%
W5142
 
5.3%
S5142
 
5.3%
O5142
 
5.3%
:5142
 
5.3%
45024
 
5.1%
24593
 
4.7%
54504
 
4.6%
34471
 
4.6%
Other values (9)15529
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)97698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034601
35.4%
18408
 
8.6%
W5142
 
5.3%
S5142
 
5.3%
O5142
 
5.3%
:5142
 
5.3%
45024
 
5.1%
24593
 
4.7%
54504
 
4.6%
34471
 
4.6%
Other values (9)15529
15.9%

Web of Science Record
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size251.2 KiB
0
5142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5142
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05142
100.0%

Length

2026-01-14T11:02:17.432537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T11:02:17.516496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
05142
100.0%

Most occurring characters

ValueCountFrequency (%)
05142
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05142
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05142
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05142
100.0%

doi_norm
Text

Missing 

Distinct4191
Distinct (%)> 99.9%
Missing950
Missing (%)18.5%
Memory size333.0 KiB
2026-01-14T11:02:17.772597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length43
Mean length25.056536
Min length13

Characters and Unicode

Total characters105037
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4190 ?
Unique (%)> 99.9%

Sample

1st row10.1007/978-3-319-52691-1_18
2nd row10.1145/3345120.3345134
3rd row10.1109/ic3.2018.00-45
4th row10.1145/3629296.3629361
5th row10.1145/3649217.3653565
ValueCountFrequency (%)
10.1145/3408877.34396392
 
< 0.1%
10.1080/03057267.2021.19635801
 
< 0.1%
10.1145/3514262.35143111
 
< 0.1%
10.1109/weit.2013.271
 
< 0.1%
10.1007/978-3-031-38454-7_121
 
< 0.1%
10.1109/icetc.2009.161
 
< 0.1%
10.4028/www.scientific.net/amm.373-375.22001
 
< 0.1%
10.1002/cae.226691
 
< 0.1%
10.1145/3345120.33451341
 
< 0.1%
10.1007/s10639-022-11454-11
 
< 0.1%
Other values (4181)4181
99.7%
2026-01-14T11:02:18.202512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (33)22448
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (33)22448
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (33)22448
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)105037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
116924
16.1%
016709
15.9%
.8905
 
8.5%
28130
 
7.7%
37450
 
7.1%
95354
 
5.1%
55080
 
4.8%
44866
 
4.6%
74849
 
4.6%
84322
 
4.1%
Other values (33)22448
21.4%

Interactions

2026-01-14T11:01:13.318829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.143754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:58.645899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:00.904686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:02.847938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:04.797982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:07.463717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:09.975474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.174534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.424703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.332801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:59.033428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:01.122256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:03.072145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:04.975148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:07.659310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:10.357298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.301497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.526840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.468639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:59.312739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:01.335986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:03.301083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:05.181493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:07.962432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:10.716130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.445042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.640674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.621176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:59.672011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:01.591647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:03.520633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:05.393348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:08.385724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:11.010560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.575849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.748631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.815155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:59.909731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:01.799462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:03.729478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:05.590564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:08.616174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:11.193436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.699296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.878633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:57.931760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:00.118572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:02.047120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:03.958939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:05.784117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:08.963813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:11.375474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.839884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.974885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:00:58.088252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:00.315212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:02.267153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:04.202217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:06.762625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:09.228877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:11.573551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.968363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:14.087705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T11:01:06.968034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:09.516126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:11.788420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T11:00:58.469210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:00.716247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:02.666111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:04.624874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:07.181373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:09.703534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:12.014874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T11:01:13.199545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T11:02:18.329564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
180 Day Usage CountBook DOIBook Group AuthorsCited Reference CountDocument TypeLanguageNumber of PagesOpen Access DesignationsPublication TypePublication YearPubmed IdSince 2013 Usage CountSpecial IssueSupplementTimes Cited, All DatabasesTimes Cited, WoS CoreUnnamed: 0Web of Science Index
180 Day Usage Count1.0001.0001.0000.5650.0290.0000.5320.1350.0720.5160.3440.6630.0001.0000.2630.248-0.0380.088
Book DOI1.0001.0001.0000.2420.2421.0000.7690.7910.7690.7920.0001.0000.0000.0001.0001.0000.2480.775
Book Group Authors1.0001.0001.0000.0000.7280.2141.0000.1020.4450.3810.0001.0000.0000.0001.0001.0000.0380.829
Cited Reference Count0.5650.2420.0001.0000.2490.0000.7730.0680.2560.4870.1520.5080.0000.0000.3310.3180.1050.211
Document Type0.0290.2420.7280.2491.0000.0000.3310.1290.8220.2110.2940.1040.0000.0000.0960.0920.0980.437
Language0.0001.0000.2140.0000.0001.0000.0000.0830.0430.0481.0000.0000.0000.0000.0000.0000.0310.065
Number of Pages0.5320.7691.0000.7730.3310.0001.0000.0000.1020.4720.1350.4880.0001.0000.2470.2310.1070.117
Open Access Designations0.1350.7910.1020.0680.1290.0830.0001.0000.2420.2140.3690.1261.0001.0000.0000.0000.0360.223
Publication Type0.0720.7690.4450.2560.8220.0430.1020.2421.0000.2910.5360.1491.0001.0000.0000.0000.1220.791
Publication Year0.5160.7920.3810.4870.2110.0480.4720.2140.2911.0000.9590.0810.1840.000-0.258-0.2630.0650.229
Pubmed Id0.3440.0000.0000.1520.2941.0000.1350.3690.5360.9591.000-0.3571.0000.000-0.621-0.624-0.1360.370
Since 2013 Usage Count0.6631.0001.0000.5080.1040.0000.4880.1260.1490.081-0.3571.0000.0720.0000.6360.616-0.1060.154
Special Issue0.0000.0000.0000.0000.0000.0000.0001.0001.0000.1841.0000.0721.0001.0001.0001.0000.0000.000
Supplement1.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0000.3780.000
Times Cited, All Databases0.2631.0001.0000.3310.0960.0000.2470.0000.000-0.258-0.6210.6361.0001.0001.0000.984-0.0240.102
Times Cited, WoS Core0.2481.0001.0000.3180.0920.0000.2310.0000.000-0.263-0.6240.6161.0001.0000.9841.000-0.0190.094
Unnamed: 0-0.0380.2480.0380.1050.0980.0310.1070.0360.1220.065-0.136-0.1060.0000.378-0.024-0.0191.0000.087
Web of Science Index0.0880.7750.8290.2110.4370.0650.1170.2230.7910.2290.3700.1540.0000.0000.1020.0940.0871.000

Missing values

2026-01-14T11:01:14.641531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T11:01:15.369311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-14T11:01:17.278608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0Publication TypeAuthorsBook AuthorsBook EditorsBook Group AuthorsAuthor Full NamesBook Author Full NamesGroup AuthorsArticle TitleSource TitleBook Series TitleBook Series SubtitleLanguageDocument TypeConference TitleConference DateConference LocationConference SponsorConference HostAuthor KeywordsKeywords PlusAbstractAddressesAffiliationsReprint AddressesEmail AddressesResearcher IdsORCIDsFunding OrgsFunding Name PreferredFunding TextCited ReferencesCited Reference CountTimes Cited, WoS CoreTimes Cited, All Databases180 Day Usage CountSince 2013 Usage CountPublisherPublisher CityPublisher AddressISSNeISSNISBNJournal AbbreviationJournal ISO AbbreviationPublication DatePublication YearVolumeIssuePart NumberSupplementSpecial IssueMeeting AbstractStart PageEnd PageArticle NumberDOIDOI LinkBook DOIEarly Access DateNumber of PagesWoS CategoriesWeb of Science IndexResearch AreasIDS NumberPubmed IdOpen Access DesignationsHighly Cited StatusHot Paper StatusDate of ExportUT (Unique WOS ID)Web of Science Recorddoi_norm
00CWeiguoZouNaNKim, YNaNWeiguoZouNaNNaNComputational Thinking Ability Training in College Computer TeachingPROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND TECHNOLOGY EDUCATION (ICSSTE 2015)Advances in Social Science Education and Humanities ResearchNaNEnglishProceedings PaperInternational Conference on Social Science and Technology Education (ICSSTE)APR 11-12, 2015Sanya, PEOPLES R CHINAInt Assoc Cyber Sci & EngnNaNComputational Thinking; Computer Teaching; Blended Teaching ModelNaNComputational thinking is ubiquitous, and has become a hot spot in education. As the core mission of college computer teaching, computational thinking training naturally causes widespread concern in the field of basic computer education. In this study, we propose the computational thinking formation procedural model, which reveals that computational thinking is the unity of internal and process, and the computational thinking ability training process is an important reason for the formation of computational thinking ability. And based on the factors of computational thinking training process, we construct the computational thinking - based blended teaching model.Yancheng Inst Ind Technol, Yancheng 224001, Peoples R ChinaNaNWeiguoZou (autor correspondiente), Yancheng Inst Ind Technol, Yancheng 224001, Peoples R China.NaNNaNNaNNaNNaNNaNNaN511238ATLANTIS PRESSPARIS29 AVENUE LAVMIERE, PARIS, 75019, FRANCE2352-5398NaN978-94-62520-60-8ADV SOC SCI EDUC HUMNaNNaN201518NaNNaNNaNNaNNaN817821NaNNaNNaNNaNNaN5Education & Educational Research; Social Sciences, InterdisciplinaryConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Education & Educational Research; Social Sciences - Other TopicsBD3LMNaNNaNNaNNaN2025-12-30WOS:0003598637002110NaN
11BRepenning, A; Basawapatna, AR; Escherle, NANaNRich, PJ; Hodges, CBNaNRepenning, Alexander; Basawapatna, Ashok R.; Escherle, Nora A.NaNNaNPrinciples of Computational Thinking ToolsEMERGING RESEARCH, PRACTICE, AND POLICY ON COMPUTATIONAL THINKINGEducational Communications and Technology-Issues and InnovationsNaNEnglishArticle; Book ChapterNaNNaNNaNNaNNaNComputational Thinking Process; Three stages of the Computational Thinking Process; Computational Thinking Tools; Principles of Computational Thinking ToolsENDComputational Thinking is a fundamental skill for the twenty-first century workforce. This broad target audience, including teachers and students with no programming experience, necessitates a shift in perspective toward Computational Thinking Tools that not only provide highly accessible programming environments but explicitly support the Computational Thinking Process. This evolution is crucial if Computational Thinking Tools are to be relevant to a wide range of school disciplines including STEM, art, music, and language learning. Computational Thinking Tools must help users through three fundamental stages of Computational Thinking: problem formulation, solution expression, and execution/evaluation. This chapter outlines three principles, and employs AgentCubes online as an example, on how a Computational Thinking Tool provides support for these stages by unifying human abilities with computer affordances.[Repenning, Alexander; Escherle, Nora A.] Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland; [Basawapatna, Ashok R.] SUNY Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USAFHNW University of Applied Sciences & Arts Northwestern Switzerland; State University of New York (SUNY) System; SUNY Old WestburyRepenning, A (autor correspondiente), Univ Appl Sci & Arts Northwestern Switzerland FHN, Sch Educ, CH-5210 Windisch, Switzerland.alexander.repenning@fhnw.ch; basawapatnaa@oldwestbury.edu; nora.escherle@fhnw.chNaNREPENNING, ALEXANDER/0000-0002-2165-7533NaNNaNNaNNaN491622058SPRINGER INTERNATIONAL PUBLISHING AGCHAMGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLANDNaNNaN978-3-319-52691-1; 978-3-319-52690-4EDUC COMMUN TECHNOLEduc. Commun. Technol.NaN2017NaNNaNNaNNaNNaNNaN291305NaN10.1007/978-3-319-52691-1_180.010.1007/978-3-319-52691-1NaN15Education & Educational Research; Social IssuesBook Citation Index– Social Sciences & Humanities (BKCI-SSH)Education & Educational Research; Social IssuesBJ2PENaNNaNNaNNaN2025-12-30WOS:000419747600019010.1007/978-3-319-52691-1_18
22CShih, WCNaNNaNAssoc Comp MachineryShih, Wen-ChungNaNNaNIntegrating Computational Thinking into the Process of Learning Artificial IntelligenceICEMT 2019: 2019 3RD INTERNATIONAL CONFERENCE ON EDUCATION AND MULTIMEDIA TECHNOLOGYNaNNaNEnglishProceedings Paper3rd International Conference on Education and Multimedia Technology (ICEMT)JUL 22-25, 2019Nagoya, JAPANNaNNaNComputational thinking; Experiential learning; Artificial intelligenceNaNIn recent years, computational thinking has once again received attention widely. Computational thinking is generally considered to be the ability to be acquired. However, this study is to use computational thinking as part of the learning method. In order to explore the application of computational thinking in teaching, this study first collected the main review papers, as well as the literature on the assessment of computational thinking, and examined their views. Then, this study proposes a learning method that integrates computational thinking into experiential learning theory and applies it to learning artificial intelligence techniques.[Shih, Wen-Chung] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, TaiwanAsia University TaiwanShih, WC (autor correspondiente), Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan.wjshih@asia.edu.twNaNShih, Wen-Chung/0000-0003-4838-8473Ministry of Science and Technology of Republic of China [MOST 106-2511-S-468 -005 -MY2]Ministry of Science and Technology of Republic of China(Ministry of Science and Technology, Taiwan)This research was supported by Ministry of Science and Technology of Republic of China under the number of MOST 106-2511-S-468 -005 -MY2.NaN2845367ASSOC COMPUTING MACHINERYNEW YORK1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATESNaNNaN978-1-4503-7210-7NaNNaNNaN2019NaNNaNNaNNaNNaNNaN364368NaN10.1145/3345120.33451340.0NaNNaN5Computer Science, Interdisciplinary Applications; Computer Science, Theory & MethodsConference Proceedings Citation Index - Science (CPCI-S)Computer ScienceBO5OPNaNNaNNaNNaN2025-12-30WOS:000518574900075010.1145/3345120.3345134
33CWu, SYNaNNaNIEEEWu, Sheng-YiNaNNaNThe Development and Challenges of Computational Thinking Board Games2018 FIRST INTERNATIONAL COGNITIVE CITIES CONFERENCE (IC3 2018)NaNNaNEnglishProceedings Paper1st International Cognitive Cities Conference (IC3)AUG 07-09, 2018Okinawa, JAPANIEEE Comp Soc,Okinawa Inst Sci & TechnolNaNcomputational thinking; board game; coding; game-based learningNaNThe promotion of computational thinking education has become a worldwide trend. To cultivate the computational thinking ability of children at young age, many computational thinking board games have appeared recently. This article introduces five computational thinking board games, including Robot Turtles, King of Pirates, Doggy Code, ROBOT WARS Coding Board Game, and Code master, and then to analyze its characteristics respectively. Additionally, this article also points out the current limitations and challenges of computational thinking board games. We hope more schools or operators will join the development of computational thinking education in the future.[Wu, Sheng-Yi] Natl Pingtung Univ, Dept Sci Commun, Pingtung, TaiwanNational Pingtung UniversityWu, SY (autor correspondiente), Natl Pingtung Univ, Dept Sci Commun, Pingtung, Taiwan.digschool@gmail.comWu, Sheng-Yi/C-4143-2011Wu, Sheng-Yi/0000-0003-3022-1843NaNNaNNaNNaN1178137IEEENEW YORK345 E 47TH ST, NEW YORK, NY 10017 USANaNNaN978-1-5386-5059-2NaNNaNNaN2018NaNNaNNaNNaNNaNNaN129131NaN10.1109/IC3.2018.00-450.0NaNNaN3Computer Science, Interdisciplinary Applications; Engineering, Electrical & ElectronicConference Proceedings Citation Index - Science (CPCI-S)Computer Science; EngineeringBM3GWNaNNaNNaNNaN2025-12-30WOS:000462080100026010.1109/ic3.2018.00-45
44CLu, CJ; Zhang, S; Chen, XQNaNChang, TNaNLu Changjin; Zhang Shuai; Chen XiuqiongNaNNaNThe Study on Computational Thinking2012 INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MANAGEMENT INNOVATION (ERMI 2012), VOL 1NaNNaNEnglishProceedings PaperInternational Conference on Education Reform and Management Innovation (ERMI 2012)DEC 04-05, 2012Shenzhen, PEOPLES R CHINANaNNaNinformation process; computational thinking; talent-training strategyNaNThis article makes an introduction to the research of computational thinking on its form, characteristic and present situation, analyses the background and the reasons for the rise of computational thinking, draws the conclusion that computational thinking should be considered from national strategic level of talent-training, and proposes some countermeasures of computational thinking enhancement, talent-training and information process.[Lu Changjin; Zhang Shuai; Chen Xiuqiong] Sanming Univ, Dept Math & Comp Sci, Sanming, Fujian Province, Peoples R ChinaSanming UniversityNaNsmlcj123@163.comNaNNaNNaNNaNNaNNaN300021INFORMATION ENGINEERING RESEARCH INST, USANEWARK100 CONTINENTAL DR, NEWARK, DE 19713 USANaNNaN978-1-16275-049-1NaNNaNNaN2013NaNNaNNaNNaNNaNNaN376380NaNNaNNaNNaNNaN5Economics; Education & Educational Research; ManagementConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Business & Economics; Education & Educational ResearchBGC87NaNNaNNaNNaN2025-12-30WOS:0003223455000730NaN
55CZhang, W; Song, LL; Huang, XJ; Wang, YNaNACMNaNZhang, Wei; Song, Lingling; Huang, Xujun; Wang, YiNaNNaNConstruction and Practice of Computational Thinking Structural Framework with Sternberg's Intellectual Education TheoryPROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY AND COMPUTERS, ICETC 2023NaNNaNEnglishProceedings Paper15th International Conference on Education Technology and Computers (ICETC)SEP 26-28, 2023Univ Barcelona, Barcelona, SPAINGrup Recerca Ensenyament Aprenentatge Virtual,Univ Warwick,Tecnologico MonterreyUniv Barcelonacomputational thinking; intelligence theories; software development; technology; project-based teachingNaNCultivating students' computational thinking skills is one of the important teaching goals of college computer courses, and constructing a scientific and effective structural framework for computational thinking is the basis for implementing computational thinking training. To this end, based on Sternberg's intelligence education theory, combined with software development and programming knowledge, a structural framework of computational thinking was constructed. Subsequently, a project-based teaching activity based on this framework to cultivate students' computational thinking skills was carried out in the college computer programming course Software Development Technology. By measuring students' computational thinking skills before and after the activity, it was verified that the project-based teaching model based on the proposed framework had a significant effect on improving students' computational thinking skills. By constructing a structural framework of computational thinking that integrates the characteristics of disciplines, and taking programming teaching as a carrier to implement the cultivation of computational thinking skills, it can provide a new way for computer educators to cultivate students' computational thinking skills.[Zhang, Wei; Song, Lingling] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China; [Huang, Xujun] Zhongnan Univ Econ & Law, Sch Foreign Studies, Wuhan, Peoples R China; [Wang, Yi] Guiyang Big Data Applicat Serv Ctr, Guiyang, Peoples R ChinaCentral China Normal University; Zhongnan University of Economics & LawSong, LL (autor correspondiente), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.zwccnu@ccnu.edu.cn; linglingsong@mails.ccnu.edu.cn; hxj168168@zuel.edu.cn; wlqyy_fam@163.comNaNNaNNational Natural Science Foundation of China [61977031]National Natural Science Foundation of China(National Natural Science Foundation of China (NSFC))This work has been finally supported by the National Natural Science Foundation of China (Grant No.61977031) named Research on the Intelligent Comprehensive Assessment of Computational Thinking for the Key Competence.NaN1400415ASSOC COMPUTING MACHINERYNEW YORK1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATESNaNNaN979-8-4007-0911-1NaNNaNNaN2023NaNNaNNaNNaNNaNNaN404408NaN10.1145/3629296.36293610.0NaNNaN5Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific DisciplinesConference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Computer Science; Education & Educational ResearchBW5RONaNNaNNaNNaN2025-12-30WOS:001166851900062010.1145/3629296.3629361
66CPasterk, S; Benke, GNaNNaNACMPasterk, Stefan; Benke, GertraudNaNNaNComputational Thinking for Self-Regulated LearningPROCEEDINGS OF THE 2024 CONFERENCE INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, VOL 1, ITICSE 2024NaNNaNEnglishProceedings Paper29th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE)JUL 08-10, 2024Univ Milano, Milan, ITALYAssoc Comp Machinery,Assoc Comp Machinery Special Interest Grp Comp Sci Educ,ACM Europe Council,Informat Europe,Github Educ,Univ Milano, Dipartimento InformaticaUniv Milanoself-regulated learning; computational thinking; secondary schoolNaNIn this theoretical paper, we compare computational thinking and self-regulated learning. Many studies use self-regulated learning to foster the acquisition of computational thinking competencies. Self-regulated learning skills are themselves beneficial for any learning process; here, we argue that the relationship between self-regulated learning and computational thinking is closer than the simple observation that self-regulated learning strategies support the acquisition of computational thinking competencies. We sustain that self-regulated learning and computational thinking competencies share many features (and have some differences), which would support synergistic effects so that not only can self-regulated learning be used to attain computational thinking competencies, but computational thinking activities can also be used to foster features of self-regulated learning competencies.[Pasterk, Stefan; Benke, Gertraud] Univ Klagenfurt, Klagenfurt, AustriaUniversity of KlagenfurtPasterk, S (autor correspondiente), Univ Klagenfurt, Klagenfurt, Austria.stefan.pasterk@aau.at; gertraud.benke@aau.atNaNBenke, Gertraud/0000-0002-6710-191XNaNNaNNaNNaN3511324ASSOC COMPUTING MACHINERYNEW YORK1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATESNaNNaN979-8-4007-0600-4NaNNaNNaN2024NaNNaNNaNNaNNaNNaN640645NaN10.1145/3649217.36535650.0NaNNaN6Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific DisciplinesConference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Computer Science; Education & Educational ResearchBX2PGNaNNaNNaNNaN2025-12-30WOS:001265872600093010.1145/3649217.3653565
77CChen, L; Xia, JX; Tao, JNaNNaNIEEEChen, Li; Xia, Jiaoxiong; Tao, JieNaNNaNCultivating Computational Thinking Among Students Of Liberal Art In Basic Computer Courses2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI)International Conference on Systems and InformaticsNaNEnglishProceedings Paper5th International Conference on Systems and Informatics (ICSAI)NOV 10-12, 2018Nanjing, PEOPLES R CHINAShanghai Dianji Univ, Sch Elect & Informat,IEEE Syst, Man, & Cybernet SocNaNComputational Thinking; Computer Foundation; Students of Liberal Art; Educational ReformNaNWith the concept of Computational Thinking proposed, how to cultivate student's Computational Thinking in basic computer courses has become the focus of basic computer education reform in recent years. Computational Thinking has gradually been recognized by computer educators and has become an important goal of basic computer education. According to the author's experience, the paper analyzes the reasons why students of literal arts also need to acquire Computational Thinking after summarizing the current research status of Computational Thinking at home and abroad. Regarding the cultivation of Computational Thinking ability as a higher level than knowledge learning and skill training, the paper puts forward some improving suggestions on how to cultivate Computational Thinking among students of liberal arts.[Chen, Li; Xia, Jiaoxiong; Tao, Jie] Shanghai Int Studies Univ, Xianda Coll Econ & Humanities, Shanghai 200083, Peoples R ChinaShanghai International Studies University; Xianda College of Economics & Humanities Shanghai International Studies UniversityChen, L (autor correspondiente), Shanghai Int Studies Univ, Xianda Coll Econ & Humanities, Shanghai 200083, Peoples R China.NaNNaNNaNSecond Round of Research Projects for Shanghai Private Colleges [2016-SHNGE-08ZD]Second Round of Research Projects for Shanghai Private CollegesThis Research was financially supported by the Second Round of Research Projects for Shanghai Private Colleges (2016-SHNGE-08ZD, Thanks for the help.NaN800016IEEENEW YORK345 E 47TH ST, NEW YORK, NY 10017 USA2474-0217NaN978-1-7281-0120-0INT CONF SYST INFORMNaNNaN2018NaNNaNNaNNaNNaNNaN544548NaNNaNNaNNaNNaN5Engineering, Electrical & ElectronicConference Proceedings Citation Index - Science (CPCI-S)EngineeringBM1JJNaNNaNNaNNaN2025-12-30WOS:0004598815000960NaN
88CRepenning, A; Basawapatna, A; Escherle, NNaNBlackwell, A; Plimmer, B; Stapleton, GNaNRepenning, Alexander; Basawapatna, Ashok; Escherle, NoraNaNNaNComputational Thinking Tools2016 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC)Symposium on Visual Languages and Human Centric Computing VL HCCNaNEnglishProceedings PaperIEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)SEP 04-08, 2016Cambridge, ENGLANDIEEENaNcomputational thinking tools; end-user programming; K-12 education; computational thinkingNaNComputational Thinking is an essential skill for all students in the 21st Century. A fundamental question is how can we create computer affordances to empower novice teachers and students, in a variety of STEM and art disciplines, to think computationally while avoiding difficult overhead emerging from traditional coding? Over the last 20 years we have iteratively developed tools that aim to support computational thinking. As these tools evolved a philosophy emerged to support Computational Thinking by joining human abilities with computer affordances. Chief among these findings is that supporting Computational Thinking is much more than making coding accessible. Computational Thinking Tools aim to minimize coding overhead by supporting users through three fundamental stages of the Computational Thinking development cycle: problem formulation, solution expression, and solution execution/evaluation.[Repenning, Alexander] Univ Colorado, Boulder, CO 80309 USA; [Repenning, Alexander; Escherle, Nora] FHNW, Sch Educ, CH-5200 Brugge, Switzerland; [Basawapatna, Ashok] SUNY Coll Old Westbury, Dept Math & Comp Informat Syst, Old Westbury, NY 11568 USAUniversity of Colorado System; University of Colorado Boulder; State University of New York (SUNY) System; SUNY Old WestburyRepenning, A (autor correspondiente), Univ Colorado, Boulder, CO 80309 USA.;Repenning, A (autor correspondiente), FHNW, Sch Educ, CH-5200 Brugge, Switzerland.NaNNaNREPENNING, ALEXANDER/0000-0002-2165-7533Hasler Foundation; National Science Foundation [0833612, 1345523, 0848962]; Direct For Education and Human Resources; Division Of Research On Learning [0833612] Funding Source: National Science Foundation; Directorate For Engineering [0848962, 1345523] Funding Source: National Science Foundation; Div Of Industrial Innovation & Partnersh [1345523, 0848962] Funding Source: National Science FoundationHasler Foundation; National Science Foundation(National Science Foundation (NSF)); Direct For Education and Human Resources; Division Of Research On Learning(National Science Foundation (NSF)NSF - Directorate for STEM Education (EDU)); Directorate For Engineering(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG)); Div Of Industrial Innovation & Partnersh(National Science Foundation (NSF)NSF - Directorate for Engineering (ENG))This work is supported by the Hasler Foundation and the National Science Foundation under Grant Numbers 0833612, 1345523, and 0848962. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these foundations.NaN372735055IEEENEW YORK345 E 47TH ST, NEW YORK, NY 10017 USA1943-6092NaN978-1-5090-0252-8S VIS LANG HUM CEN CNaNNaN2016NaNNaNNaNNaNNaNNaN218222NaNNaNNaNNaNNaN5Computer Science, Software Engineering; Computer Science, Theory & MethodsConference Proceedings Citation Index - Science (CPCI-S)Computer ScienceBG8AXNaNNaNNaNNaN2025-12-30WOS:0003921580000340NaN
99JEzeamuzie, NO; Leung, JSCNaNNaNNaNEzeamuzie, Ndudi O.; Leung, Jessica S. C.NaNNaNComputational Thinking Through an Empirical Lens: A Systematic Review of LiteratureJOURNAL OF EDUCATIONAL COMPUTING RESEARCHNaNNaNEnglishReviewNaNNaNNaNNaNNaNcomputational thinking; problem solving; algorithms; programming; abstractionELEMENTARY CLASSROOMS; COMPUTER-SCIENCE; ROBOTICS; SKILLS; PAPER; EXPLORATION; VIEWPOINT; EDUCATION; VALIDITY; DESIGNThis article provides an overview of the diverse ways in which computational thinking has been operationalised in the literature. Computational thinking has attracted much interest and debatably ranks in importance with the time-honoured literacy skills of reading, writing, and arithmetic. However, learning interventions in this subject have modelled computational thinking differently. We conducted a systematic review of 81 empirical studies to examine the nature, explicitness, and patterns of definitions of computational thinking. Data analysis revealed that most of the reviewed studies operationalised computational thinking as a composite of programming concepts and preferred definitions from assessment-based frameworks. On the other hand, a substantial number of the studies did not establish the meaning of computational thinking when theorising their interventions nor clearly distinguish between computational thinking and programming. Based on these findings, this article proposes a model of computational thinking that focuses on algorithmic solutions supported by programming concepts which advances the conceptual clarity between computational thinking and programming.[Ezeamuzie, Ndudi O.; Leung, Jessica S. C.] Univ Hong Kong, Fac Educ, Hong Kong, Hong Kong, Peoples R ChinaUniversity of Hong KongEzeamuzie, NO (autor correspondiente), Univ Hong Kong, Fac Educ, Hong Kong, Hong Kong, Peoples R China.amuzie@connect.hku.hkLeung, Jessica Shuk Ching/G-4619-2013; Ezeamuzie, Ndudi Okechukwu/ABG-1289-2021Ezeamuzie, Ndudi Okechukwu/0000-0001-8946-5709NaNNaNNaNNaN100688629393SAGE PUBLICATIONS INCTHOUSAND OAKS2455 TELLER RD, THOUSAND OAKS, CA 91320 USA0735-63311541-4140NaNJ EDUC COMPUT RESJ. Educ. Comput. Res.APR2022602NaNNaNNaNNaN481511735633121103315810.1177/073563312110331580.0NaNJUL 202131Education & Educational ResearchSocial Science Citation Index (SSCI)Education & Educational Research0H2KINaNNaNNaNNaN2025-12-30WOS:000678265200001010.1177/07356331211033158
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51325132JGoldenberg, EP; Carter, CJNaNNaNNaNGoldenberg, E. Paul; Carter, Cynthia J.NaNNaNProgramming as a language for young children to express and explore mathematics in schoolBRITISH JOURNAL OF EDUCATIONAL TECHNOLOGYNaNNaNEnglishArticleNaNNaNNaNNaNNaNactivity‐ based learning; coding; constructionism; mathematics; primary educationEARLY-CHILDHOOD MATHEMATICSNatural language helps express mathematical thinking and contexts. Conventional mathematical notation (CMN) best suits expressions and equations. Each is essential; each also has limitations, especially for learners. Our research studies how programming can be a advantageous third language that can also help restore mathematical connections that are hidden by topic-centred curricula. Restoring opportunities for surprise and delight reclaims mathematics' creative nature. Studies of children's use of language in mathematics and their programming behaviours guide our iterative design/redesign of mathematical microworlds in which students, ages 7-11, use programming in their regular school lessons as a language for learning mathematics. Though driven by mathematics, not coding, the microworlds develop the programming over time so that it continues to support children's developing mathematical ideas. This paper briefly describes microworlds EDC has tested with well over 400 7-to-8-year-olds in school, and others tested (or about to be tested) with over 200 8-to-11-year-olds. Our challenge was to satisfy schools' topical orientation and fit easily within regular classroom study but use and foreshadow other mathematical learning to remove the siloes. The design/redesign research and evaluation is exploratory, without formal methodology. We are also more formally studying effects on children's learning. That ongoing study is not reported here. Practitioner notes What is already known Active learning-doing-supports learning. Collaborative learning-doing together-supports learning. Classroom discourse-focused, relevant discussion, not just listening-supports learning. Clear articulation of one's thinking, even just to oneself, helps develop that thinking. What this paper adds The common languages we use for classroom mathematics-natural language for conveying the meaning and context of mathematical situations and for explaining our reasoning; and the formal (written) language of conventional mathematical notation, the symbols we use in mathematical expressions and equations-are both essential but each presents hurdles that necessitate the other. Yet, even together, they are insufficient especially for young learners. Programming, appropriately designed and used, can be the third language that both reduces barriers and provides the missing expressive and creative capabilities children need. Appropriate design for use in regular mathematics classrooms requires making key mathematical content obvious, strong and the 'driver' of the activities, and requires reducing tech 'overhead' to near zero. Continued usefulness across the grades requires developing children's sophistication and knowledge with the language; the powerful ways that children rapidly acquire facility with (natural) language provides guidance for ways they can learn a formal language as well. Implications for policy and/or practice Mathematics teaching can take advantage of the ways children learn through experimentation and attention to the results, and of the ways children use their language brain even for mathematics. In particular, programming-in microworlds driven by the mathematical content, designed to minimise distraction and overhead, open to exploration and discovery en route to focused aims, and in which children self-evaluate-can allow clear articulation of thought, experimentation with immediate feedback. As it aids the mathematics, it also builds computational thinking and satisfies schools' increasing concerns to broaden access to ideas of computer science.[Goldenberg, E. Paul] Educ Dev Ctr EDC, 43 Foundry Ave, Waltham, MA 02453 USA; [Carter, Cynthia J.] Rashi Sch, Dedham, MA USAEducation Development Center (EDC)Goldenberg, EP (autor correspondiente), Educ Dev Ctr EDC, 43 Foundry Ave, Waltham, MA 02453 USA.pgoldenberg@edc.orgNaNNaNNaNNaNNaNNaN381118764WILEYHOBOKEN111 RIVER ST, HOBOKEN 07030-5774, NJ USA0007-10131467-8535NaNBRIT J EDUC TECHNOLBr. J. Educ. Technol.MAY2021523NaNNaNNaNNaN969985NaN10.1111/bjet.130800.0NaNNaN17Education & Educational ResearchSocial Science Citation Index (SSCI)Education & Educational ResearchSK0OCNaNhybridNaNNaN2025-12-30WOS:000655921600002010.1111/bjet.13080
51335133JRahimi, S; Almond, R; Ramírez-Salgado, A; Wusylko, C; Weisberg, L; Song, Y; Lu, J; Myers, T; Wang, BW; Wang, XM; Francois, M; Moses, J; Wright, ENaNNaNNaNRahimi, Seyedahmad; Almond, Russell; Ramirez-Salgado, Andrea; Wusylko, Christine; Weisberg, Lauren; Song, Yukyeong; Lu, Jie; Myers, Ted; Wang, Bowen; Wang, Xiaomaon; Francois, Marc; Moses, Jennifer; Wright, EricNaNNaNCompetency model development: The backbone of successful stealth assessmentsJOURNAL OF COMPUTER ASSISTED LEARNINGNaNNaNEnglishArticleNaNNaNNaNNaNNaNassessment for learning; competency model; digital games; evidence cantered design; learning analytics; stealth assessmentDIVERGENT THINKING; CREATIVITYBackgroundStealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real-time inferences about their learning. Employed in digital learning environments, stealth assessment helps researchers, educators, and teachers evaluate learners' competencies and customize the learning experience to their specific needs. This adaptability is closely intertwined with theories related to learning, engagement, and motivation. The foundation of stealth assessment rests on evidence-cantered design (ECD), consisting of four core models: the Competency Model (CM), Evidence Model, Task Model, and Assembly Model.ObjectiveThe first step in designing a stealth assessment entails producing operational definitions of the constructs to be assessed. The CM establishes a framework of latent variables representing the target constructs, as well as their interrelations. When developing the CM, assessment designers must produce clear descriptions of the claims associated with the latent variables and their states, as well as sketch out how the competencies can be measured using assessment tasks. As the designers elaborate on the assessment model, the CM definitions need to be revisited to make sure they work with the scope and constraints of the assessment. Although this is the first step, problems at this stage may result in an assessment that does not meet the intended purpose. The objective of this paper is to elucidate the necessary steps for CM development and to highlight potential challenges in the process, along with strategies for addressing them, particularly for designers without much formal assessment experience.MethodThis paper is a methodological exposition, showcasing five examples of CM development. Specifically, we conducted a qualitative retrospective analysis of the CM development procedure, wherein participants unfamiliar with ECD applied the framework and showcased their work. In a stealth assessment course, four groups of students (novice stealth assessment designers) engaged in developing stealth assessments for challenging-to-measure constructs across four distinct projects. During their CM development process, we observed various activities to pinpoint areas of difficulty.ResultsThis paper presents five illustrative examples, including one for assessing physics understanding and four for the development of CMs for four complex competencies: (1) systems thinking, (2) online information credibility evaluation, (3) computational thinking, and (4) collaborative creativity. Each example represents a case in CM development, offering valuable insights.ConclusionThe paper concludes by discussing several guidelines derived from the examples discussed. Emphasizing the importance of dedicating ample time to fine-tune CMs can significantly enhance the accuracy of assessments related to learners' knowledge and skills. It underscores the significance of qualitative phases in crafting comprehensive stealth assessments, such as CMs, alongside the quantitative statistical modeling and technical aspects of these assessments. What is currently known about this topic? Stealth assessment represents an unobtrusive, automated formative assessment method. This method uses learning analytics within digital learning environments (e.g., games). The main purpose is to assess and foster the competencies of diverse learners.What does this paper add? This paper serves as a conceptual and methodological guide. This paper focuses on the critical process of competency model development. Competency model development is a crucial step in the creation of stealth assessments.Implications for practice/or policy Learning scientists and assessment designers can leverage this paper as a resource. Assessment designers can benefit from this paper and see various examples of the process.[Rahimi, Seyedahmad; Ramirez-Salgado, Andrea; Wusylko, Christine; Weisberg, Lauren; Song, Yukyeong; Lu, Jie; Myers, Ted; Wang, Bowen; Wang, Xiaomaon; Francois, Marc; Moses, Jennifer; Wright, Eric] Univ Florida, Sch Teaching & Learning, Gainesville, FL USA; [Almond, Russell] Florida State Univ, Educ Psychol & Learning Syst, Tallahassee, FL USA; [Rahimi, Seyedahmad] Univ Florida, Coll Educ, Sch Teaching & Learning, 2403 Norman Hall, Gainesville, FL 32611 USAState University System of Florida; University of Florida; State University System of Florida; Florida State University; State University System of Florida; University of FloridaRahimi, S (autor correspondiente), Univ Florida, Coll Educ, Sch Teaching & Learning, 2403 Norman Hall, Gainesville, FL 32611 USA.srahimi@ufl.eduWeisberg, Lauren/M-1489-2019; Wang, Bowen/KXR-3143-2024; Almond, Russell/GQB-0824-2022; Francois, Marc/JCN-8502-2023; Wusylko, Christine/JLK-9388-2023; Ramirez-Salgado, Andrea/ACY-2548-2022Weisberg, Lauren/0000-0001-8033-3662; Lu, Jie Jennifer/0000-0002-7466-6177; Rahimi, Seyedahmad/0000-0001-9266-758X; Almond, Russell/0000-0002-8876-9337;NaNNaNNaNNaN6333630WILEYHOBOKEN111 RIVER ST, HOBOKEN 07030-5774, NJ USA0266-49091365-2729NaNJ COMPUT ASSIST LEARJ. Comput. Assist. Learn.DEC2024406NaNNaNNaNNaN27722789NaN10.1111/jcal.130250.0NaNJUN 202418Education & Educational ResearchSocial Science Citation Index (SSCI)Education & Educational ResearchN5B3VNaNBronzeNaNNaN2025-12-30WOS:001243574300001010.1111/jcal.13025
51345134CRonsivalle, GB; Boldi, A; Giunta, ENaNChova, LG; Martinez, AL; Torres, ICNaNRonsivalle, G. B.; Boldi, A.; Giunta, E.NaNNaNIMPROVING THE TRAINING PROCESS: A COURSE TO HELP EDUCATORS LEADING EFFECTIVELY CODING ACTIVITIES10TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION (ICERI2017)ICERI ProceedingsNaNEnglishProceedings Paper10th Annual International Conference of Education, Research and Innovation (ICERI)NOV 16-18, 2017Seville, SPAINNaNNaNCoding; training; educators; computational thinking; innovation; schoolNaNCoding is a powerful instrument to include and integrate the different learning styles and the cognitive peculiarities of every student: writing clear and unequivocal instructions, using a machine language and working with a concrete tool could lead students to be more accurate, concise and conscious about their own process of learning. Coding lessons motivate student learning scientific disciplines, overcoming those emotional factors obstructing a fertile experience of school. Because of the advantages offered, Coding has been fully introduced in ministerial programs in Italy as a discipline: it is not only an innovative subject for pedagogy in theoretical terms, but it requires schools to rapidly and effectively comply with reforms implemented since 2014. OECD TALIS 2013 data confirms teachers need training, as they are required to teach and use Coding as a school discipline, but they do not have the required competencies: based on this, in 2015 the first project was launched to better introduce Coding in the Italian Schools. However, the current training paths which are offered to educators, in Italy and abroad, present several shortcomings: poor timing, absence of evaluation moments and, consequently, lack of tools for analysis of results. In order to overcome these critical issues, we propose and describe the design of a training course addressed to educators. This path is tailored to meet the real needs and the characteristics of the recipients, it refers to a rigorous design method and it is able to quantify the actual increase of the skills of the subjects. This method could support the teachers in organizing educational interventions, avoiding the use of improvisation, increasing the internal validity of the course and allowing replication in other contexts. First of all, we provide an overview of the Coding projects launched in Italian schools: method, structure, monitoring data and first results. Then, we identify the best practices and the lesson learned which are summarized in a guideline. We finally propose a course path, observing some important quality criteria to self-evaluate the quality of our work. In particular, we apply three main quality indicators: 1. The quality of the design: a) preliminary analysis, b) macro-design and c) micro-design; 2. The quality provided: a) pre-delivery and b) delivery; 3. Quality of the results' evaluation: a) evaluation and b) reporting. The course design consists of two essential moments: I) A first phase, macro-design, which illustrates the general structure of the course. The three main outputs of this phase are: 1. the conceptual map, which has been created to identify the conceptual nodes and the main contents of the course; 2. the tree of didactic objectives, each of one is defined according to the Bloom complexity level ( revised): we also list the main assessment tests, consistent with the level of the Bloom's Taxonomy; 3. a flow chart, defining the logical structure, the rules and the time scanning; II) A second phase, micro-design, which describes the storyboard of the classroom and the rating storyboard. Following this method, we believe teachers could put in practice programs which are tailored to their particular class. Going through different experimentations, teachers could also promote the use of coding in interdisciplinary domains that are not yet coded also supporting research in this field.[Ronsivalle, G. B.] Univ Verona, Verona, Italy; [Boldi, A.] Wemole Srl, Rome, ItalyUniversity of VeronaRonsivalle, GB (autor correspondiente), Univ Verona, Verona, Italy.NaNBoldi, Arianna/ADK-4069-2022NaNNaNNaNNaNNaN140005IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENTVALENICALAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN2340-1095NaN978-84-697-6957-7ICERI PROCNaNNaN2017NaNNaNNaNNaNNaNNaN69396959NaNNaNNaNNaNNaN21Education & Educational ResearchConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Education & Educational ResearchBJ9XBNaNNaNNaNNaN2025-12-30WOS:0004299753070100NaN
51355135CDiaz, L; Foster, T; Barashango, SCNaNNaNASSOC COMPUTING MACHINERYDiaz, Lien; Foster, Terry; Barashango, Sababu ChakaNaNNaNDoes the Advanced Placement Computer Science (CS) Principles course drive equitable and inclusive CS pedagogy, curriculum, and policy as a means to broaden participation in computing?PROCEEDINGS OF THE CONFERENCE FOR RESEARCH ON EQUITABLE AND SUSTAINED PARTICIPATION IN ENGINEERING, COMPUTING, AND TECHNOLOGY, RESPECT 2024NaNNaNEnglishProceedings PaperConference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)MAY 16-17, 2024Atlanta, GAAssoc Comp Machinery,ACM Special Interest Grp Comp Sci EducNaNAdvanced Placement Computer Science Principles; equity; inclusion; CS education policyNaNThe premise for the development of the Advanced Placement (AP) Computer Science Principles course was aimed at broadening participation in computing, as a high school level CS course. Since AP courses carry credibility with millions of students who take AP Exams as they are recognized with prospects of obtaining a college education, the hope was that the AP CS Principles course would lead to increased participation in AP CS Exams, especially with students historically excluded in CS including girls, Black, Hispanic, and Native American students, as well those with disabilities. The course raises opportunities and access to CS in higher education. The AP CS Principles curriculum framework is used in the development of the Exam which is significant in the creation college credit and placement policies. Nearly 1,300 colleges and universities have created policies providing students with opportunities to receive college credit or placement for scoring a 3 or higher on the AP CS Principles Exam [12]. The AP CS Principles curriculum framework is also used to define the learning outcomes for the course and stands as a pivotal tool in shaping high school CS education pathways to post-secondary introductory CS courses: It was designed to meet rigorous content requirements of an innovative first semester college-level introductory CS course. It exposes students to demanding expectations of building high levels of computational thinking skills and practical applications of programming that are valuable as they advance in their academics. It provides opportunities for students to connect fundamental programming concepts with important topics such as understanding the role of data in programming, and how data is processed and analyzed. AP CS Principles also recognizes the societal impacts of technology and teaches students about ethical considerations that may arise when analyzing bias in technological systems so that students develop a well-rounded perspective on technology's role in society [5]. Additional themes such as the infrastructure of the Internet including networks and protocols are also included. This paper focuses on the vision of the AP CS Principles course underpinnings (a) being engaging and appealing to a wider range of students, (b) making it accessible for a more racially, ethnically, and gender-identity diverse population of high school students, and (c) providing the benefits of the AP label on students' high school transcripts gives them options to consider a pathway into college CS studies with an enhanced admissions appeal, potential academic scholarships, and/or careers in the field. We investigate the structure of the AP CS Principles curriculum framework as a key resource that impacts the kinds of teaching and learning that is promoted in the Course and Exam Description. We discuss our experiences with the imbalanced emphasis on inclusive pedagogy and building community within the classroom to directly increase sense of belonging with students historically excluded from computing. Lastly, while the AP CS Principles Exam continues to flourish in participation numbers, we question the effectiveness of policies to promote broadening participation in computing. We review policies from three different states and discuss how they leverage the AP CS Principles course to promote teacher certification and student enrollment but do not necessarily ensure equitable practices to promote diverse representation in terms of gender, race, socioeconomic background, and disability.[Diaz, Lien; Foster, Terry; Barashango, Sababu Chaka] Georgia Tech, Constellat Ctr Equ Comp, Atlanta, GA 30332 USAUniversity System of Georgia; Georgia Institute of TechnologyDiaz, L (autor correspondiente), Georgia Tech, Constellat Ctr Equ Comp, Atlanta, GA 30332 USA.ldiaz@cc.gatech.edu; terry.foster@cc.gatech.edu; sababu.barashango@cc.gatech.eduNaNNaNNaNNaNNaNNaN172212ASSOC COMPUTING MACHINERYNEW YORK1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATESNaNNaN979-8-4007-0626-4NaNNaNNaN2024NaNNaNNaNNaNNaNNaN158162NaN10.1145/3653666.36562810.0NaNNaN5Education, Scientific Disciplines; Social Issues; Social Sciences, InterdisciplinaryConference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Education & Educational Research; Social Issues; Social Sciences - Other TopicsBX0CANaNgoldNaNNaN2025-12-30WOS:001227772300024010.1145/3653666.3656281
51365136CRonsivalle, GB; Boldi, A; Bazzi, CNaNChova, LG; Martinez, AL; Torres, ICNaNRonsivalle, G. B.; Boldi, A.; Bazzi, C.NaNNaNEDUCATIONAL TECHNOLOGIES FOR SPECIFIC LEARNING DISORDERS (SLD) IN PRIMARY SCHOOL: A LECTURE OF CODING DESIGNED WITH COMPENSATING WRITING SOFTWARE10TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION (ICERI2017)ICERI ProceedingsNaNEnglishProceedings Paper10th Annual International Conference of Education, Research and Innovation (ICERI)NOV 16-18, 2017Seville, SPAINNaNNaNSLDs; Coding; technology; software; compensatory toolsNaNThe diffusion of Specific Learning Disabilities (SLDs), in an evolving theoretical and clinical scenario, afflicts the ability of experts to diagnose, differentiate and provide early support of students suffering from an SLD. It is necessary to correctly assess the presence of this type of neurodiversity in the learning style since the pupil attends Primary School, limiting the important damage caused by not considering the emotional and relational framework beyond the cognitive one. Considering the SLDs as a category including various ways of processing information, the purpose for those who work in the education field is to find new strategies of teaching in the classroom. Currently, there are several technology tools with a compensating purpose which have a different effect on the main ways an SLD child organizes, processes, accesses and uses information. Among these, Coding has become world-class for the ability to develop a paradigm shift in the way an SLD person thinks, which is the main issue of the disorder. Coding is a powerful instrument to include and integrate the different learning styles and the cognitive peculiarities of every student: coding 1) reinforces some critical skills (problem solving, the sense of orientation, logical-computational thinking and ability to synthesize information); 2) it generates positive emotionality in the child by involving him in a fun and motivating activity; 3) it offers strategies for dealing with dysgraphia. However, Coding is an instrument and it should be integrated as a part of a training path in order to be effective. The learning courses have to be both designed according to a rigorous method and tailored-made for the specific context (classroom, children, training need analysis). The paper describes in detail the design of a standard-lesson to teach Coding in schools. First of all, we put together the existing platform of coding with compensating software Super Quaderno, a special text editor which can overcome the problems of coordination and short-term memory typical of dysgraphia thanks to its features: phonetic spelling, multimedia objects, automatic association of images, reading word-by-word. The lesson is structured into a) a series of stimulating questions to introduce the topic, dealing with the main resistances and correcting wrong believes; b) a role play that introduce the learners to machine language, stimulating their ability to communicate in a simple and clear way, which is essential for programming; c) a creative exercise that consists in creating an ex novo story using visual blocks of programming: in the first phase the pupil writes a short story, which will be associated with images and sounds; in the second phase, the images are arranged in order to create a labyrinth, while the text disappears; in the last phase, the pupil has to follow the path, made of images and synthesized sounds, in order to recreate the original story. The purpose is to a) stimulate the mind to identify the logical and chronological sense of a text, b) train writing in a ludic manner and c) enhance the typical learning style of children with SLDs, giving way to their talents and creativity. This collaboration can also be extended to other software, to create an interconnected network between programming and compensation tools, potentially useful in different school environments.[Ronsivalle, G. B.] Univ Verona, Verona, Italy; [Boldi, A.] Wemole Srl, Rome, Italy; [Bazzi, C.] Don G Busato, Vicenza, ItalyUniversity of VeronaRonsivalle, GB (autor correspondiente), Univ Verona, Verona, Italy.NaNBoldi, Arianna/ADK-4069-2022NaNNaNNaNNaNNaN1011112IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENTVALENICALAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN2340-1095NaN978-84-697-6957-7ICERI PROCNaNNaN2017NaNNaNNaNNaNNaNNaN69306938NaNNaNNaNNaNNaN9Education & Educational ResearchConference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)Education & Educational ResearchBJ9XBNaNNaNNaNNaN2025-12-30WOS:0004299753070090NaN
51375137JOrtiz, LG; Bekerman, Z; Ros, MZNaNNaNNaNOrtiz, Lourdes Guardia; Bekerman, Zvi; Ros, Miguel ZapataNaNNaNPresentation of the special issue Generative AI, ChatGPT and Education. Consequences for Intelligent Learning and Educational EvaluationRED-REVISTA DE EDUCACION A DISTANCIANaNNaNEnglishArticleNaNNaNNaNNaNNaNChatGPT; Generative AI; education; artificial intelligence; instructional design; assessmentCOMPUTATIONAL THINKINGIn July 2023, given the rise of LLMs (Large Langauge Models), RED convened this special issue on Generative AI and Education, where special attention was paid to its consequences for intelligent learning and educational evaluation. We wanted to give space to contributions that included research related to these topics. Also to experiences about intelligent learning and formative evaluation in ChatGPT contexts. The call was with these general questions center dot Does AI have the potential to revolutionize existing teaching methods, assessment and student support? center dot Creative thinking and problem solving are essential in modern and very complex environments. Could this AI help students deal with these problems? We also had doubts about its benefits. They could be summarized in this question: Generative AI will begin to serve as an active partner in social, creative and intellectual actions continuously over time, and not only as an answer to isolated questions: What are the impacts that will occur? Now those impacts are unknown in the practices that may exist. Another intention was: A theoretical framework is needed to address these questions and in general for an effective deployment of AI systems in education. It is necessary to do so beyond the results provided by empirical research. And that it does not guide and direct at new crossroads, both in research and practice. In the conclusions we see to what extent these expectations have been met. As a consequence, we deduce that the critical importance of theory in the design, development and deployment of AI in education is necessary now more than ever. But we are equally underserved. In this perspective, we continue to critically consider the relevance and continuity of existing learning theories when AI becomes a reality in classrooms. As that result is not met, we also reiterate the call to consider new frameworks, models and ways of thinking. We are referring to those that include the presence of non -human agents, which we hesitate to call a new technology, because it is more like an active partner than a simple technology, as has happened until now. This approach is precisely what makes us insist on a series of important questions for the future, precisely about the review of learning theories based on existing configurations. And to investigate what their alternatives would be in this case. We have done after extensive and exhaustive dissemination in your call. But, despite this and beyond these general conclusions that we have made, the special issue offers us evidence of a scarce empirical investigation of practical cases in the application of generative AI in education. However, of the hundred or so contributions received, seven have been selected in the previous editorial review. The rest have been discarded because they do not conform to the standards or are not the type of contributions requested (the literature reviews per se and the self-report studies stand out among them, due to their high number). Of those seven, six have passed editorial review. They are described at the end. The main contributions of this small number of contributions have been the confirmation of a low level of research and practice. Also, some very interesting contributions from the articles and essays by the invited authors. We draw your attention to these articles and the clear results and evidence obtained on the concrete use of generative AI in specific environments. Results of inevitable use by schools, universities and teachers in these environments or in others to which they can be transferred.[Ortiz, Lourdes Guardia] Univ Oberta Catalunya, Barcelona, Spain; [Bekerman, Zvi] Hebrew Univ Jerusalem, Jerusalem, Israel; [Ros, Miguel Zapata] Univ Murcia, Murcia, SpainUOC Universitat Oberta de Catalunya; Hebrew University of Jerusalem; University of MurciaOrtiz, LG (autor correspondiente), Univ Oberta Catalunya, Barcelona, Spain.lguardia@uoc.edu; zvi.bekerman@mail.huji.ac.il; mzapata@um.esBEKERMAN, ZVI/A-1521-2010BEKERMAN, ZVI/0000-0002-3493-0770NaNNaNNaNNaN252618109UNIV MURCIAMurciaEdificio Pleiades Campus de Espinardo, Murcia, 30071, SPAIN1578-7680NaNNaNRED-REV EDUC DISTANCRED-Rev. Educ. DistanciaMAY 3020242478NaNNaNNaNNaNNaNNaNNaN10.6018/red.6098010.0NaNNaN19Education & Educational ResearchEmerging Sources Citation Index (ESCI)Education & Educational ResearchTA3E6NaNgoldNaNNaN2025-12-30WOS:001238488500002010.6018/red.609801
51385138JTariq, R; Abatal, M; Vargas, J; Vázquez-Sánchez, AYNaNNaNNaNTariq, Rasikh; Abatal, Mohamed; Vargas, Joel; Vazquez-Sanchez, Alma YolandaNaNNaNDeep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass wasteSCIENTIFIC REPORTSNaNNaNEnglishArticleNaNNaNNaNNaNNaN3-nitrophenol; Carbonaceous materials; Haematoxylum campechianum; Deep learning; Artificial neural network; Genetic algorithm; Educational innovation; Higher education; Computational thinking; Artificial intelligenceACTIVATED CARBON; PHENOLIC-COMPOUNDS; SUBSTITUTED PHENOLS; AQUEOUS-SOLUTION; RHIZOPUS-ARRHIZUS; CONGO RED; REMOVAL; WATER; NITROPHENOLS; CHLOROPHENOLSThe presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC's surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3-8), adsorbent dosages (2-10 g/L), and temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich-Peterson models. PSO kinetics demonstrated a good fit (R-2 > 0.98) for Ci = 50-100 mg/L, indicating a chemical adsorption mechanism. The Langmuir isotherm model exhibited the best fit, confirming chemical adsorption, with a maximum adsorption capacity (Q(m)) of 236.156 mg/g at T = 300.15 K, pH = 6, contact time = 3 h, and 2 g/L adsorbent dosage. Lower temperatures favored exothermic adsorption. Artificial neural networks (ANNs) were employed for deep learning, optimizing the predictive model for removal percentage. Correlation heat maps highlighted positive correlations between time, dosage, and removal percentage, emphasizing the impact of initial concentration on efficiency. ANN modeling, incorporating iterative optimization, yielded highly accurate predictions, aligned closely with experimental results. The study showcases the success of deep learning in optimizing adsorption processes, emphasizing the importance of diverse correlation algorithms for comprehensive insights into competitive adsorption dynamics. The 5-14-14-1 deep learning architecture, fine-tuned over 228 epochs, demonstrated strong performance with mean squared error (MSE) values of 4.07, 18.406, and 6.2122 for training, testing, and total datasets, respectively, and high R-squared values. Graphical analysis showed a solid linear correlation between experimental and simulated removal percentages, emphasizing the need to consider more than just testing data for optimization. Experimental validation confirmed a 98.77% removal efficiency, illustrating the effectiveness of combining deep learning with genetic algorithms, and highlighting the necessity of experimental trials to verify computational predictions. It is concluded that the carbonaceous material from Haematoxylum campechianum waste (CM-HC) is an effective, low-cost adsorbent for removing 3-nitrophenol from aqueous solutions, achieving optimal removal at pH 6 and 300. 15 K with a maximum adsorption capacity of 236.156 mg/g, following Langmuir model and pseudo-second order kinetics. The validated ANN model offers a reliable tool for practical applications in environmental remediation, advancing both environmental science and educational innovation by integrating artificial neural networks and data science methodologies into student learning experiences.[Tariq, Rasikh] Tecnol Monterrey, Inst Future Educ, Ave Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico; [Abatal, Mohamed] Univ Autonoma Carmen, Fac Ingn, Ave Cent S-N Esq Con Fracc Mundo Maya, Ciudad Del Carmen 24115, Campeche, Mexico; [Vargas, Joel] Univ Nacl Autonoma Mexico, Unidad Morelia, Inst Invest Mat, Antigua Carretera Patzcuaro 8701,Col Ex Hacienda S, Morelia 58190, Michoacan, Mexico; [Vazquez-Sanchez, Alma Yolanda] Univ Tecnol Xicotepec Juarez, Area Agroind Alimentaria, Ave Univ Tecnol 1000,Col Tierra Negra Xicotepec de, Xicotepec De Juarez 73080, Puebla, MexicoTecnologico de Monterrey; Universidad Autonoma del Carmen; Universidad Nacional Autonoma de MexicoAbatal, M (autor correspondiente), Univ Autonoma Carmen, Fac Ingn, Ave Cent S-N Esq Con Fracc Mundo Maya, Ciudad Del Carmen 24115, Campeche, Mexico.mabatal@pampano.unacar.mxAbatal, Mohamed/G-6047-2018; Tariq, Rasikh/AAO-6006-2020; Vargas, Joel/OJV-5248-2025; Alma Yolanda, Vázquez Sánchez/AAP-4797-2021Alma Yolanda, Vázquez Sánchez/0000-0003-4230-6975Tecnologico de Monterrey [IJXT070-23EG99001]Tecnologico de Monterrey(Instituto Politecnico Nacional - Mexico)The authors would like to thank Tecnologico de Monterrey for the financial support provided through the 'Challenge-Based Research Funding Program 2023', Project ID #IJXT070-23EG99001, entitled 'Complex Thinking Education for All (CTE4A): A Digital Hub and School for Lifelong Learners.'NaN1071313930NATURE PORTFOLIOBERLINHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY2045-2322NaNNaNSCI REP-UKSci RepAUG 302024141NaNNaNNaNNaNNaNNaN2025010.1038/s41598-024-70989-00.0NaNNaN29Multidisciplinary SciencesScience Citation Index Expanded (SCI-EXPANDED)Science & Technology - Other TopicsE6N2X39215127.0Green Submitted, goldNaNNaN2025-12-30WOS:001304147400093010.1038/s41598-024-70989-0
51395139CMcMullin, BNaNAndrews, P; Caves, L; Doursat, R; Hickinbotham, S; Polack, F; Stepney, S; Taylor, T; Timmis, JNaNMcMullin, BarryNaNNaNThe Overshoot Curriculum: Artificial Life, Education and the Human PredicamentECAL 2015: THE THIRTEENTH EUROPEAN CONFERENCE ON ARTIFICIAL LIFENaNNaNEnglishProceedings Paper13th European Conference on Artificial Life (ECAL)JUL 20-24, 2015Univ York, York Ctr Complex Syst Anal, York, ENGLANDMIT Press, Artificial Life,Springer,HIERATIC Project Hierarch Anal Complex Dynam Syst,FoCAS Project Fundamentals Collect Adapt Syst,Earth Life Sci Inst Origins Network,SimOmicsUniv York, York Ctr Complex Syst AnalNaNNaNIt is well established in the scientific literature that global human civilization is in serious ecological trouble. The most comprehensive survey is perhaps that of the Planetary Boundaries framework (Rockstrom et al., 2009). The unfolding of these challenges will, of course, be a very complex process; and some detailed impacts are certainly still open to significant human management and moderation. Nonetheless, it seems clear that we are no longer dealing with a problem, or even set of problems, that might be solved; rather, this is a predicament - an uncertain, dynamic, and at least partially chaotic, disruption in global human development (Gilding, 2012). A predicament calls not for solution, but for engagement, and continuous, long term, refinement of response. The purpose of this contribution is to propose a particular educational (curricular and pedagogic) response: one that specifically draws on the tools, techniques and understandings of the field of Artificial Life. In recent decades, the mission of university education, at least in public universities, has become progressively identified simply with the direct support of economic development in its sponsoring regional community. That is, its primary role is to provide graduates with just the knowledge and skills most immediately aligned with the preceived needs of the regional economy. There is a perfectly clear logic and rationale to this development; but in a world faced with global ecological disruption, within the lifetimes of current students, this is neither an honest nor even an effective preparation for the challenges which they will face. Moreover, given that even limited moderation of the impending impacts of ecological limits will rely on the widest societal understanding and deeply informed and engaged leadership at all levels, it is arguable that universities have an immediate, and potentially decisive role to play in communicating the realities of the current global ecological situation. And so, to Artificial Life; or more precisely, to Artificial Ecology. The use of computational tools to model complex biological, evolutionary, ecological and social dynamics is a foundational technique in the ALife field. Indeed, computational thinking and modelling was at the heart of the systems dynamics approach to socio-ecological modelling pioneered by Forrester (1982). This provided the basis for the famous (or infamous?) Limits to Growth (LTG) project of the Club of Rome (Meadows et al., 1972). This was the first substantive attempt to computationally model the socio-ecological dynamics of global human society and assess whether ecological impacts would be likely to limit the growth of human material activities within any practically foreseeable time-frame. While the model was necessarily crude, the robust result was that - in the absence of effective control measures to the contrary - serious limits would become apparent within the first half of the 21st century. In the 40 years since, the world has tracked remarkably close to the standard run of the LTG study (Turner, 2014). In fact, multiple lines of investigation now strongly suggest not just that aggregate human activity is approaching ecological limits, but that it has already reached a state of significant overshoot beyond those limits. Overshoot is a qualitatively distinct regime for the design and operation of any adaptive or mitigating interventions (Catton, 1982). Effective societal responses to date have been significantly impaired by a lack of wide understanding of this harsh ecological reality. This gap in understanding facilitates the comforting - but erroneous - notion that it is prudent to delay difficult responses until after impacts are manifest. But delay is precisely one of the principle mechanisms that actually causes overshoot, and undermines the capability to damp the subsequent crash. Accordingly, it is suggested that there is now a clear need to develop what is here termed an overshoot curriculum, and to integrate this with formation of graduates in all disciplines. Further, a key pedagogical technique in delivering such a curriculum will be the systematic use of computational model ecologies. The Alife community is therefore uniquely positioned to contribute to this radical reform of higher education to meet what are, without exaggeration, the most profound challenges in the history of human civilization.[McMullin, Barry] Dublin City Univ, Dublin 9, IrelandDublin City UniversityMcMullin, B (autor correspondiente), Dublin City Univ, Dublin 9, Ireland.barry.mcmullin@dcu.ieMcMullin, Barry/B-9504-2009McMullin, Barry/0000-0002-5789-2068NaNNaNNaNNaN60002MIT PRESSCAMBRIDGEONE ROGERS ST, CAMBRIDGE, MA 02142 USANaNNaNNaNNaNNaNNaN2015NaNNaNNaNNaNNaNNaN414414NaN10.7551/978-0-262-33027-5-ch0730.0NaNNaN1Computer Science, Interdisciplinary Applications; Evolutionary Biology; Mathematical & Computational BiologyConference Proceedings Citation Index - Science (CPCI-S)Computer Science; Evolutionary Biology; Mathematical & Computational BiologyBO3ESNaNNaNNaNNaN2025-12-30WOS:000510147800073010.7551/978-0-262-33027-5-ch073
51405140JBasaran, M; Vural, ÖF; Metin, S; Tamur, SNaNNaNNaNBasaran, Mehmet; Vural, omer Faruk; Metin, Sermin; Tamur, SabihaNaNNaNEarly Coding Education and its Multidimensional Impact on Preschool Development: An Analysis of ChatGPT's InsightsINTERNATIONAL JOURNAL OF EARLY CHILDHOODNaNNaNEnglishArticleNaNNaNNaNNaNNaNCoding education; Preschool education; ChatGPT insights; Preschool developmentCOMPUTATIONAL THINKING; CHILDHOOD; INTERNET; CHILDREN; LEARNThis study investigates ChatGPT's perspectives on coding education for preschool children to provide a comprehensive understanding that is valuable for educators in early childhood education. An instrumental case study approach was employed, utilizing qualitative research design and case study methods. Data were gathered using a structured interview form containing 93 questions posed to ChatGPT version 3.5 to obtain in-depth insights into coding education during the preschool period. Content analysis examined ChatGPT's responses, identifying key themes and codes. The findings indicate that coding practices equip children with coding skills and support their development in multiple domains. Furthermore, the study highlights that uncertainties surrounding the definition of coding, the skills it encompasses, and its integration into curricula are gradually diminishing, with more explicit frameworks emerging. While there are drawbacks to artificial intelligence tools, the study concludes that tools like ChatGPT have significant potential to contribute to the content development of teachers, offering valuable resources to enhance coding education in early childhood. Este estudio investiga las perspectivas de ChatGPT sobre la educaci & oacute;n en programaci & oacute;n para ni & ntilde;os en edad preescolar, con el objetivo de proporcionar una comprensi & oacute;n integral valiosa para los educadores en la educaci & oacute;n infantil. Se emple & oacute; un enfoque de estudio de caso instrumental, utilizando un dise & ntilde;o de investigaci & oacute;n cualitativo y m & eacute;todos de estudio de caso. Los datos se recopilaron mediante un formulario de entrevista estructurada que conten & iacute;a 93 preguntas planteadas a la versi & oacute;n 3.5 de ChatGPT para obtener informaci & oacute;n profunda sobre la educaci & oacute;n en programaci & oacute;n durante el per & iacute;odo preescolar. El an & aacute;lisis de contenido examin & oacute; las respuestas de ChatGPT, identificando temas y c & oacute;digos clave. Los hallazgos indican que las pr & aacute;cticas de programaci & oacute;n equipan a los ni & ntilde;os con habilidades de programaci & oacute;n y apoyan su desarrollo en m & uacute;ltiples & aacute;reas. Adem & aacute;s, el estudio destaca que las incertidumbres en torno a la definici & oacute;n de la programaci & oacute;n, las habilidades que abarca y su integraci & oacute;n en los planes de estudio est & aacute;n disminuyendo gradualmente, con la aparici & oacute;n de marcos m & aacute;s expl & iacute;citos. Si bien existen inconvenientes en las herramientas de inteligencia artificial, el estudio concluye que herramientas como ChatGPT tienen un potencial significativo para contribuir al desarrollo de contenidos para los docentes, ofreciendo recursos valiosos para mejorar la educaci & oacute;n en programaci & oacute;n en la primera infancia. Cette & eacute;tude explore les perspectives de ChatGPT sur l'& eacute;ducation & agrave; la programmation pour les enfants d'& acirc;ge pr & eacute;scolaire, dans le but de fournir une compr & eacute;hension globale pr & eacute;cieuse pour les & eacute;ducateurs en & eacute;ducation de la petite enfance. Une approche d'& eacute;tude de cas instrumental a & eacute;t & eacute; utilis & eacute;e, en adoptant un design de recherche qualitatif et des m & eacute;thodes d'& eacute;tude de cas. Les donn & eacute;es ont & eacute;t & eacute; recueillies & agrave; l'aide d'un formulaire d'entretien structur & eacute; comprenant 93 questions pos & eacute;es & agrave; la version 3.5 de ChatGPT afin d'obtenir des informations approfondies sur l'& eacute;ducation & agrave; la programmation durant la p & eacute;riode pr & eacute;scolaire. Une analyse de contenu a & eacute;t & eacute; r & eacute;alis & eacute;e pour examiner les r & eacute;ponses de ChatGPT, identifiant des th & egrave;mes et codes cl & eacute;s. Les r & eacute;sultats indiquent que les pratiques de programmation dotent les enfants de comp & eacute;tences en codage et soutiennent leur d & eacute;veloppement dans plusieurs domaines. En outre, l'& eacute;tude met en lumi & egrave;re que les incertitudes concernant la d & eacute;finition de la programmation, les comp & eacute;tences qu'elle englobe et son int & eacute;gration dans les programmes scolaires diminuent progressivement, avec l'& eacute;mergence de cadres plus explicites. Bien qu'il existe des limites aux outils d'intelligence artificielle, l'& eacute;tude conclut que des outils comme ChatGPT ont un potentiel significatif pour contribuer au d & eacute;veloppement de contenus pour les enseignants, en offrant des ressources pr & eacute;cieuses pour am & eacute;liorer l'& eacute;ducation & agrave; la programmation en petite enfance.[Basaran, Mehmet; Tamur, Sabiha] Gaziantep Univ, Fac Educ, Dept Educ Sci, Gaziantep, Turkiye; [Vural, omer Faruk] Sakarya Univ, Fac Educ, Dept Educ Sci, Sakarya, Turkiye; [Metin, Sermin] Hasan Kalyoncu Univ, Fac Educ, Dept Presch Teacher Educ, Gaziantep, TurkiyeGaziantep University; Sakarya University; Hasan Kalyoncu UniversityBasaran, M (autor correspondiente), Gaziantep Univ, Fac Educ, Dept Educ Sci, Gaziantep, Turkiye.mehmetbasaran@outlook.com; omervural@sakarya.edu.tr; sermin.metin@hku.edu.tr; tamur3373@gmail.comVURAL, OMER FARUK/W-9479-2018; metin, şermin/ABI-5631-2020; Başaran, Mehmet/AAG-5084-2020VURAL, OMER FARUK/0000-0002-1520-3762; metin, şermin/0000-0001-5984-6359; Tamur, Sabiha/0000-0001-6894-1243; Başaran, Mehmet/0000-0003-1871-520XNaNNaNNaNNaN90121431SPRINGERDORDRECHTVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS0020-71871878-4658NaNINT J EARLY CHILDInt. J. Early ChildAUG2025572NaNNaNNaNNaN575601NaN10.1007/s13158-024-00411-30.0NaNJAN 202527Education & Educational ResearchEmerging Sources Citation Index (ESCI)Education & Educational Research6VN8VNaNNaNNaNNaN2025-12-30WOS:001398678700001010.1007/s13158-024-00411-3
51415141JMorales-Navarro, L; Fields, D; Kafai, YB; Barapatre, DNaNNaNNaNMorales-Navarro, Luis; Fields, Deborah; Kafai, Yasmin B.; Barapatre, DeepaliNaNNaNAssessing changes in thinking about troubleshooting in physical computing: a clinical interview protocol with failure artifacts scenariosINFORMATION AND LEARNING SCIENCESNaNNaNEnglishArticleNaNNaNNaNNaNNaNDebugging; Troubleshooting; Computer science education; Clinical interview; Assessment; Electronic textiles; Physical computingCOMPUTATIONAL THINKING; DESIGN; EXPLANATIONS; PROGRAMSPurposeThe purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students' explanations of troubleshooting processes in physical computing activities. The authors focus on physical computing, as finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills.Design/methodology/approachThis paper developed and piloted a failure artifact scenarios clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. Authors applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. They analyzed matching pre- and post-interviews from 18 students at four different schools.FindingsThe findings demonstrate how the protocol can capture change in students' thinking about troubleshooting by eliciting students' explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios and concreteness of troubleshooting and problem-solving processes.Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit. edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1. Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit. edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?Originality/valueBeyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the failure artifact scenarios clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.1.Pre-unit interview protocol?Scenario 1Interviewer: I'm gonna show you two projects that are not working and I want to ask you to help me figure out how to fix them. I know you have not started the e-textiles unit, but anything you tell me will be helpful. Tell me what you are thinking and to share any ideas you may have so that I can try them later. Here is an e-textiles project that a student in another class is working on. It's not working as intended. [Interviewer shows image (see Figure A1) by sharing a document that the student can annotate. Interviewer reads the notes below the images.]Interviewer: What would be your instructions for that student to fix it? Feel free to annotate the picture, if it's helpful to you, but you don't have to. [follow-up as needed] Remember, we can ask the student to unsew some stitches and open up the toy too. [follow-up as needed] Remember, we can direct the student to look into the code as well. [follow-up as needed] Why did you say that? Scenario 2Interviewer: Here is my Captain America Shield project [show the actual project and share teacher-approved circuit drawing (see Figure A2)]. I also have a simulation of the project so that you can interact with it. Here is how it is supposed to behave [demo how it works! www.scratch.mit.edu/projects/479329018/fullscreen/]: if both buttons are pushed, the outer LEDs (on white ring) turn on, while the inner LEDs (on red ring) do a chase pattern; if Button1 is pushed and Button2 is not pushed, the outer LEDS (on the white ring) are off and the inner LEDs (on the red ring) blink; if Button1 is not pushed and Button2 is pushed, the outer LEDs (on the white ring) blink and the inner LEDS (on the red ring) are off; and if neither button is pushed, then all LEDs are off. Interviewer shares the link [www.scratch.mit.edu/projects/479329018/fullscreen/] with the student and requests that they share their screen.Interviewer: This project isn't working. Can you tell me how to fix it? I can write down steps to do when I go back to my workshop. Talk about everything you are thinking as you look at this project: [follow-up as needed] What do you think could be the causes for each of these issues? [follow-up as needed] How would you fix them? Why did you say that? When I leave, what should I do?[Morales-Navarro, Luis; Kafai, Yasmin B.; Barapatre, Deepali] Univ Penn, Learning Sci & Technol Program, Philadelphia, PA 19104 USA; [Fields, Deborah] Utah State Univ, Dept Instruct Technol & Learning Sci, Logan, UT USAUniversity of Pennsylvania; Utah System of Higher Education; Utah State UniversityMorales-Navarro, L (autor correspondiente), Univ Penn, Learning Sci & Technol Program, Philadelphia, PA 19104 USA.luismn@upenn.edu; deborah.fields@usu.edu; kafai@upenn.edu; dee2496@upenn.eduFields, Deborah/GXZ-6816-2022; Morales-Navarro, Luis/KDN-6301-2024NaNNational Science Foundation [1742140/#1742081]National Science Foundation(National Science Foundation (NSF))With regards to Katherine Gregory for support in data analysis. This work was supported by a grant from the National Science Foundation to Yasmin Kafai and Mike Eisenberg (#1742140/#1742081). Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF, the University of Pennsylvania or Utah State University.NaN551111EMERALD GROUP PUBLISHING LTDLeedsFloor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE, ENGLAND2398-53482398-5356NaNINFORM LEARN SCIInf. Learn. Sci.FEB 2420251263/4NaNNaNNaNNaN286312NaN10.1108/ILS-06-2024-00750.0NaNJAN 202527Information Science & Library ScienceEmerging Sources Citation Index (ESCI)Information Science & Library ScienceX5U6ENaNNaNNaNNaN2025-12-30WOS:001388694200001010.1108/ils-06-2024-0075